Background:Lumbar spinal stenosis is a leading indication for spine surgery, but outcomes are heterogeneous. We aimed to develop and externally validate prediction models for 12-month disability and pain to inform shared decision-making. Methods:This registry-based multicentre cohort study used data from three national spine registries of patients (≥16 years) undergoing elective lumbar spinal stenosis surgery. Data from the Norwegian Registry for Spine Surgery (NORspine, 2007-2023) were used for model development and internal-external cross-validation (IECV). External validation was carried out in the Swedish Registry (SweSpine, 2016-2022) and Danish Registry (DaneSpine, 2009-2022) with data collected by the Spine Centre of Southern Denmark. The primary outcome was the Oswestry Disability Index (ODI) at 12 months, modelled as a continuous and binary measure (acceptable symptom state). Secondary outcomes were Numeric Rating Scale (NRS) back and leg pain at 12 months. Logistic regression, linear regression, and XGBoost models were applied with 16 predictors. Missing data were handled using multiple imputation. Performance was assessed by calibration, mean absolute error (MAE), adjusted R2, and C-statistics. This study is registered with Open Science Framework (https://osf.io/qz27b/). Findings:The development cohort included 31,908 patients (52.4% female, 47.6% male). The external validation cohorts included 30,700 from SweSpine (52.8% female, 47.2% male) and 4063 from DaneSpine (54.6% female, 45.4% male). Twelve-month outcome completeness was 77% in the development cohort and ranged from 66% to 80% across the external validation cohorts. For ODI, linear regression achieved a pooled MAE of 12.4 (95% CI 11.8-13.1) after IECV, and 13.3 (95% CI 13.2-13.4) and 12.3 (95% CI 12.0-12.7) at external validation. Adjusted R2 values ranged from 0.26 to 0.33. Calibration was acceptable, with slopes near 1 and calibration-in-the-large ranging from -0.47 after IECV to 1.28-1.54 at external validation, indicating minor systematic underprediction. The binary ODI model achieved C-statistics of 0.75 (95% CI 0.74-0.76) after IECV, and 0.78 (95% CI 0.78-0.79) and 0.76 (95% CI 0.74-0.77) at external validation. Pain models showed lower performance (MAE 2.2-2.6; C-statistics 0.64-0.73). XGBoost yielded similar results. Interpretation:Models predicting disability and pain were well calibrated and generalisable across Scandinavian countries, with the best overall performance for disability. These findings provide a foundation for prospective evaluation in future studies to determine the impact on decision-making and patient outcomes in clinical practice. Funding:Research Council of Norway.

INTRODUCTION:Long-term sickness absence (LTSA) in young adults has important consequences for labour market participation and future work disability. Chronic pain and psychological distress are key risk factors and frequently co-occur, yet their combined impact during adolescence on later LTSA remains insufficiently understood. This study aims to explore factors that influence adolescents' and young people's risk of receiving LTSA benefits during emerging adulthood. METHODS:This longitudinal study used data from the Young-HUNT1 (1995-1997; n = 8736) and Young-HUNT3 (2006-2008; n = 7935) cohorts linked to Norwegian registry data and followed into early adulthood. The outcome was time to LTSA (≥90 or ≥180 days). Associations were examined using Cox proportional hazards models and Kaplan-Meier analyses. Continuous- and discrete-time models were developed and evaluated using the concordance index, time-dependent AUC, and integrated Brier score. Risk factors were analysed using SurvSHAP, SHAP, and regression-based methods. RESULTS:Chronic pain and co-occurring pain and psychological distress were consistently associated with increased LTSA risk (adjusted HRs between 1.3 and 1.5 for pain and between 1.6 and 1.7 for co-occurrence). In contrast, psychological distress alone showed no consistent association. Model performance was moderate and similar across approaches (C-index between 0.63 and 0.67). Key predictors included female sex, low parental education, chronic pain, poor perceived health, and indicators of early health problems. CONCLUSION:Adolescent chronic pain, particularly when co-occurring with psychological distress, is an important predictor of LTSA in early adulthood. While absolute LTSA levels may vary across cohorts, underlying risk patterns remain stable. More complex models did not outperform traditional approaches. These findings highlight the importance of early-life conditions and support early identification and intervention to reduce later work absence.

Introduction:Spinal disorders are among the biggest contributors to health care utilization (HCU). Objective:To develop and externally validate a prediction model for high all-cause HCU (75th percentile during 1 year after the index date) among patients with spinal disorders visiting multidisciplinary secondary care clinics. Methods:We developed and internally validated the model using the Norwegian Neck and Back Registry, including patients registered between January 1, 2016, and December 31, 2020, linked with national health registries (N = 9092). For external validation, we used data from the Danish SpineData Registry, linked with national registries, for the same period (N = 34,853). We assessed Nagelkerke R 2 , discrimination (area under receiver operating characteristics curve [AUC]), and calibration (calibration-in-the-large [CITL], slope, and calibration plot). Results:The final model included sex, nationality, education, physical activity, smoking, prior HCU, work status, disability, health-related quality of life, medicine use, diagnosis, kinesiophobia, and comorbidity. It demonstrated acceptable discrimination (AUC 0.78, 95% confidence interval [CI], 0.77-0.78), an R 2 of 0.26, and good calibration after internal validation. Upon external validation, the model demonstrated excellent discrimination (AUC 0.81, 95% CI 0.80-0.81) and an R 2 of 0.31. The calibration slope was 1.08 (95% CI 1.06-1.11) and CITL was 0.16 (95% CI 0.12-0.19). Predicted probabilities closely matched observed probabilities across all deciles in internal validation, with slight underestimation of high HCU in the top 3 deciles during external validation. Conclusion:Overall, the model shows promise in predicting high HCU in patients with spinal disorders referred to secondary care but requires further testing and validation in implementation settings before recommendation.

Abstract Background Rising healthcare costs and demand call for better identification of individuals at risk of high-cost healthcare use. Few prediction models use detailed survey data or address persistent high-cost use in the general population. This study aimed to develop and externally validate prediction models for all-cause single-year and persistent high-cost healthcare use, and to assess whether adding survey data to administrative registry data improved performance. Methods This was a prognostic study based on two population-based cohorts, the Trøndelag Health Study (HUNT4; model development) and the Tromsø Study (Tromsø7; external validation), linked to prospectively collected health registry data from primary and secondary care. Outcomes were (1) single-year high-cost use, defined as being in the top 25% of total healthcare costs in year one after survey completion, and (2) persistent high-cost use, defined as being in the top 25% in both years one and two. Predictors included self-reported sociodemographic and health-related variables and health registry data (prior-year costs and a morbidity index). Logistic regression models were developed for each outcome and internally validated via five-fold cross-validation. Model performance was assessed through discrimination and calibration. XGBoost models were trained and tested for benchmarking. External validation applied the developed models without refitting. We also developed and validated registry-only and survey-only models to compare performance against the full model. Results The development cohort included 42,049 individuals, and the external validation cohort included 20,942. In internal validation, the full logistic regression model achieved C-statistics of 0.79 (95% CI 0.78–0.79) for single-year high-cost use and 0.83 (95% CI 0.83–0.84) for persistent high-cost use. Corresponding C-statistics in external validation were 0.78 (95% CI 0.77–0.78) and 0.82 (95% CI 0.81–0.83). The models appeared well-calibrated on calibration plots. Full models showed significantly higher C-statistics than registry-only models (p < 0.001). Conclusion Prediction models for identifying all-cause single-year high-cost and persistent high-cost healthcare use in the general adult population were developed and validated, showing good discrimination and calibration. The models can inform targeted preventive strategies and population health management. Incorporating self-reported survey data improved predictive performance, supporting the use of combining data sources for risk stratification.

Background Socioeconomic disadvantage and health conditions may mutually reinforce. Musculoskeletal and mental health conditions are among the leading causes of early labour market exit and together account for around two-thirds of long-term sickness absence resulting in labour market dropout in Norway. Universal Basic Income (UBI) has been proposed as an intervention that may help address social determinants of these conditions. Norway may be well-placed to host a trial of UBI effects on health, but context-specific underpinning work is needed before a credible grant application can be made. As a first step, we convened a group of health and UBI researchers to consider what underpinning work is needed and what factors may influence trial design. Methods Health and UBI researchers were invited to attend a conference and workshop, during which a Technology of Participation workshop approach was used to facilitate convergence of opinion on knowledge gaps and recommend research priorities. Results Fourteen researchers attended a workshop in Oslo and found broad convergence of opinion on priorities. Commended underpinning work included research in a Norwegian context to better understand health effects of reductions in benefit income due to sanctions or conditionality ( e.g. through qualitative work or surveys), undertaking a discrete choice experiment to quantify value attributes of desirable components of a future health-focused trial of UBI in Norway ( e.g. through conjoint analysis), and agreeing on a core set of outcome measures ( e.g. within a Delphi study) to commend for use in UBI trials more generally. Conclusions Better understanding the health effects of social benefit sanctions and the values placed on intervention components in a Norwegian context may support a case for doing a health-focused trial of UBI in Norway, and understanding values placed on different intervention components will help inform the design of such a trial. Recommending core outcome sets would facilitate cross-comparisons between trials.

Healthcare utilization is high for people with spinal disorders. We aimed to investigate differences in recommended versus actual healthcare use among individuals with back neck pain compared to individuals with isolated back or neck pain. Additionally, we aimed to examine variations in healthcare utilization patterns before and after a specialist evaluation based on the location of pain. We linked the Norwegian Neck and Back Registry with health registries, including patients with spinal disorders registered between January 1st, 2016, and December 31st, 2020 (9316 patients (63

Machine-learning-enabled prognostic models are increasingly proposed to support surgical decision-making for degenerative lumbar disorders, yet their clinical adoption remains limited. Understanding how surgeons perceive these tools is critical for effective implementation, particularly given the high-stakes nature of spine surgery where decisions carry long-term functional consequences and medico-legal implications. This study aimed to explore how consultant-level spine surgeons perceive machine-learning-based prognostic tools, including their trustworthiness, clinical utility, usability, workflow integration, and implications for patient counselling and shared decision-making. A qualitative study using semi-structured one-to-one interviews was conducted with 11 consultant-level orthopaedic and neurosurgeons practising in Singapore (response rate: 73% of 15 invited). Participants had a mean of 10.9 years (range: 8-25 years) of spine surgery experience; 82% (n=9) were male. Interviews (range: 15-57 minutes) were transcribed verbatim and analysed using Braun and Clarke's six-step reflexive thematic analysis. Data collection continued until information power was achieved, with three additional interviews completed after thematic sufficiency was reached as participants had already consented. The study was designed and reported in accordance with COREQ criteria. Three overarching themes were developed. Trust contingent on data integrity revealed that surgeons' confidence depended fundamentally on data quality, local representativeness, labelling credibility, and rigorous validation, with participants consistently emphasising population representativeness as a trust prerequisite. Surgeons operationalised trust through accessible performance metrics, with most identifying AUROC as their preferred credibility heuristic. Pragmatic orientation as important for implementation demonstrated that usability and seamless electronic health record integration were non-negotiable prerequisites, with surgeons explicitly stating that manual data entry would preclude adoption. Medico-legal concerns were prominent, with participants emphasising that decision responsibility remains with the clinician. Surgical decision-making as a delicate dance between art and science reflected how prognostic outputs were positioned as adjunctive inputs to be reconciled with experiential judgement and patient heterogeneity. Surgeons emphasised that identical prognostic information would be interpreted differently based on practice philosophy, and most highlighted the inherent divergence between technical success and patient-perceived success, underscoring prognostic tools' value for expectation management rather than deterministic prediction. This first qualitative study of spine surgeons' perceptions reveals that adoption of machine-learning-based prognostic tools is contingent on data integrity, pragmatic workflow integration, and alignment with professional judgement and not predictive performance alone. Surgeons expressed cautious openness, viewing these tools as valuable in complex cases for clarifying outcome expectations without displacing clinical responsibility. Meaningful implementation requires robust data governance, contextually grounded validation, seamless electronic integration, and explicit positioning of machine learning as supportive rather than substitutive of surgical judgement. These findings provide empirically grounded guidance for developing clinically acceptable and implementation-ready prognostic decision support systems.

This study aimed to investigate the distribution of all-cause primary healthcare use (general practitioners [GPs], physiotherapists, chiropractors) and identify patient characteristics related to high use in the year following an assessment at a hospital spine clinic among patients with spinal pain. We linked self-reported data from 48,616 adult patients assessed at a Danish hospital spine clinic between 2016 and 2021 with national registry data. We investigated the distribution of consultations with GPs and physiotherapists/chiropractors in the year following the assessment using Lorenz plots. Multivariable logistic regression was used to examine associations between sociodemographic and clinical patient characteristics and high (versus low) healthcare utilization. All analyses were conducted separately for consultations with GPs and physiotherapists/chiropractors. Overall, 25

BACKGROUND:Spinal disorders are associated with substantial healthcare costs, but prognostic factors associated with high spine-related healthcare expenditures remain insufficiently understood. Therefore, this study aimed to identify prognostic factors associated with spine-related healthcare costs among patients with spinal disorders. METHODS:A prognostic factor study using data from the Norwegian Neck and Back Registry was conducted. Thirty-four potential prognostic factors were considered. Spine-related healthcare costs were analyzed as a continuous outcome. Continuous predictors were modelled using restricted cubic splines to account for potential non-linear associations. Univariable and multivariable adjusted models were fitted, with priori defined covariate adjustments. RESULTS:The study included 7877 patients with spinal pain. In adjusted models, healthcare region (patients from the Mid-Norway, West and Southeast regions incurred 69.1%, 44.5% and 29.6% higher costs, respectively, compared with those in the North), disability (90th vs. 10th percentile: +29.2%; CI +11.9% to +49.2%), pain during activity (90th vs. 10th percentile: +23.4%; CI +7.6% to +41.6%), health-related quality of life (90th vs. 10th percentile: -21.3%; CI -31.1% to -10.0%), job satisfaction (90th vs. 10th percentile: +14.7%; CI +2.3% to +28.7%), and opioid use (+15.3%; CI +5.7% to +26.2%) were associated with healthcare costs. CONCLUSIONS:This set of identified prognostic factors may be useful for characterizing patient subgroups, developing prognostic models to identify individuals at risk of high healthcare expenditures, and informing strategies to improve the efficiency and equity of healthcare resource allocation. SIGNIFICANCE:This study identifies clinical and societal determinants of health associated with high spine-related healthcare costs, highlighting how these factors jointly drive resource use. These findings help clarify mechanisms underlying cost variation and support the future development of prognostic tools and targeted strategies to improve efficiency and equity in spine care.

INTRODUCTION:Chronic pain and psychological distress significantly impact young adults' health, yet little is known about their associated factors when they co-occur. This study applied machine learning classification models and explainable AI to uncover associations across diverse factors with chronic pain, psychological distress, and their co-occurrence in young adults. It also examined how associations vary when these conditions occur individually versus when they co-occur. Variable clusters were also obtained to identify latent subgroups. METHODS:Cross-sectional data from the Norwegian Students' Health and Well-being Study (SHoT2018) were utilised. Three classification models (Logistic Regression, Tsetlin Machine, CatBoost) were developed separately for three mutually exclusive groups (chronic pain only, psychological distress only, and their co-occurrence), against a healthy group. Model performance was assessed using Area Under the Curve (AUC) and internal-external cross-validation using geographical regions as folds. Shapley values and odds ratios were applied to identify factors associated with conditions, with associations compared across them. Clause rules were obtained to search for variable clusters. RESULTS:A total of 34,469 participants (mean age 22.9 years; 67.4% female) were included. Chronic pain, psychological distress, and co-occurrence were reported by 27.2%, 12.7%, and 26.7%, respectively. Models performed consistently across regions (AUC 0.70-0.72 for chronic pain, 0.92-0.93 for psychological distress, and 0.94-0.96 for co-occurrence. Explainable AI analyses identified females, perceived health status, and sleep disorders as factors associated with all conditions. Associations shifted when conditions co-occurred versus individually, with some negating each other while others combined, revealing distinct patterns for the co-occurring group. CONCLUSION:The findings underscore the importance of studying these conditions both individually and simultaneously. Machine learning and explainable AI revealed unique associations that can help with early identification of subjects at risk.

PURPOSE:Spinal surgery outcomes vary significantly among patients, underscoring the need for objective tools to guide clinical decision-making. While previous prediction models have focused on functional and pain-specific outcomes, global patient-reported measures such as the Global Perceived Effect (GPE) remain underexplored. This study aimed to develop and temporally validate machine learning models to predict patient's GPE 12 months after lumbar disc herniation and spinal stenosis surgery. METHODS:This registry-based study used data from the Norwegian Registry for Spine Surgery. The dataset was temporally split for model development (2007-2017) and validation (2018-2021). Six supervised machine learning models, including XGBoost, Gradient Boosting, Random Forest, Multilayer Perceptron (MLP), Decision Tree, and K-Nearest Neighbors, were used to predict dichotomized GPE outcomes (success vs. non-success). Model performance was evaluated through discrimination, calibration, and decision curve analysis. RESULTS:Analyses included 13,029 patients operated for disc herniation and 18,058 for spinal stenosis. For disc herniation, the MLP model achieved the highest performance at temporal validation, with a C-statistic of 0.72 (95% CI 0.71-0.74) and good calibration. For spinal stenosis, XGBoost performed best, with C-statistic 0.67 (95% CI 0.66-0.68) and good calibration. Key predictors included pain duration, prior surgery, anxiety/depression, education level, and healthcare setting. The models demonstrated consistent clinical utility through decision curve analysis. CONCLUSIONS:Using nationwide registry data, we developed and temporally validated machine learning models to predict patient-perceived benefit one year after lumbar spine surgery. The disc herniation model showed possibly useful discrimination and good calibration, supporting its potential to inform clinical decision-making.

BACKGROUND AND OBJECTIVE:Persistent pain is common among adolescents, with significant consequences. School-based interventions have the potential to reach most adolescents, but there is a lack of systematic reviews addressing school-based pain interventions. This systematic review aimed to identify and evaluate the effectiveness of school-based interventions for reducing persistent pain and disability in adolescents compared to control interventions. DATABASES AND DATA TREATMENT:The PRISMA guidelines were followed (CRD42023477721). Searches in Medline, EMBASE, PEDro, CINAHL, Web of Science, Cochrane, AMED, PsycINFO, and Google Scholar identified 6651 studies. We included randomised controlled trials (RCTs) and controlled trials (CTs) of interventions conducted at schools involving adolescents aged 10-19 years with persistent pain lasting ≥ 3 months. Risk of bias was measured using the Cochrane RoB 2 tool for RCTs and ROBINS-I for CTs. Results were synthesised narratively. RESULTS:Sixteen studies met the inclusion criteria (n = 12 RCTs, n = 3 CTs, n = 1 pilot RCT) including 2873 participants. The identified interventions were relaxation, education, exercise, taping, and tailored pain management. Relaxation reduced headache activity in four out of five studies; exercise was effective for back and menstrual pain; education showed conflicting results; and person-centred care was not better than usual school health care for reducing pain. All included studies had a high risk of bias. CONCLUSION:Relaxation and exercise showed promising effectiveness on reducing persistent pain in adolescents, although with a high risk of bias. The need for higher-quality studies remains imperative to strengthen the evidence base and inform future interventions. SIGNIFICANCE STATEMENT:This systematic review highlights the potential of school-based interventions, particularly relaxation and exercise, in reducing persistent pain among adolescents, although in studies with a high risk of bias. These findings underscore the need for higher-quality studies to establish robust evidence and inform effective, equitable pain management strategies in school settings.

BACKGROUND:The association between different patterns of healthcare use and non-recovery in patients with spinal disorders is unclear. We aimed to assess the association between healthcare use and non-recovery 6 months after a specialist evaluation in Norwegian secondary care and whether non-recovery was linked to adherence to specialist-recommended care. METHODS:This observational registry-based cohort study includes 3745 patients aged 18-70 years (mean (SD) 46 (12) years, 59% women) from the Norwegian Neck and Back Registry (NNRR). We studied non-recovery 6 months after the specialist evaluation using the Global Perceived Effect (GPE) scale, defined as 'slightly improved', 'unchanged', 'slightly worse', 'much worse', or 'worse than ever'. Using logistic regression, we examined the association between non-recovery and specialist-recommended healthcare (i.e., recommended follow-up in primary or secondary care) and actual healthcare use identified in national registries (visits to general practitioners, physical therapists, and chiropractors in primary care and outpatient and inpatient visits in secondary care). RESULTS:In total, 80% self-reported non-recovery at 6 months. Adherence to specialist-recommended healthcare was not associated with non-recovery (adjusted OR [aOR] 1.09, 95% CI 0.91-1.29). Highest odds for non-recovery were among patients using primary care alone (aOR 1.68, 95% CI 1.37-2.07) or no healthcare (aOR 1.81, 95% CI 1.44-2.27). Secondary care alone (aOR 0.75, 95% CI 0.59-0.96) or combined with primary care (aOR 0.49, 95% CI 0.41-0.59) was associated with recovery. CONCLUSIONS:Our findings raise questions about the value of the specialist recommendations and also the content of healthcare services provided after a specialist evaluation. SIGNIFICANCE STATEMENT:Our findings suggest that follow-up after specialist evaluation may not adequately meet patient needs, indicating a need for improved management of spinal disorders. Given the low proportion of patients reporting recovery at 6-month follow-up, we highlight the importance of good transitions, care coordination, and coherent messages across sectors and professions. More effective healthcare and reduced sick leave could save societal costs. Moreover, this approach could improve quality of life, enabling a more active and participatory lifestyle.

OBJECTIVES: This study aimed to assess 12-month outcomes on return to work (RTW) and cost-effectiveness in adults on sick leave due to musculoskeletal disorders who were randomized to either usual case management (UC), UC+motivational interviewing (MI) or UC+stratified vocational advice intervention (SVAI). METHODS: The study was conducted in the Norwegian Labor and Welfare Administration (NAV). Workers on sick leave due to musculoskeletal disorders for ≥50% of their contracted work hours for ≥7 consecutive weeks were included. Trained caseworkers delivered MI in two face-to-face sessions, and physiotherapists provided SVAI and identified RTW obstacles. The main outcomes were sick leave days over 12 months and cost-effectiveness, cost-utility and cost-benefit. RESULTS: The trial included 509 workers with a mean age of 48 years. There were statistically significant differences between UC+MI versus UC [-15.6 days, 95% confidence interval (CI) -31.0– -0.2], and UC+SVAI versus UC (-17.6 days, 95% CI -33.0– -2.2). Compared to UC, odds ratios (OR) for receiving wage replacement benefits each month were lower for UC+MI (OR=0.73, 95% CI 0.64–0.84), and UC+SVAI (OR 0.74, 95% CI 0.64–0.84). The probabilities of cost-effectiveness were high for adding either MI or SVAI to UC (ceiling ratio 0.90), and the net benefit for MI was €5225 (95% CI -592–10 985) and for SVAI €7214 ((95% CI 1548–12 851) per person. CONCLUSIONS: Adding MI or SVAI to UC significantly improved RTW outcomes and was cost-effective among people on sickness absence due to musculoskeletal disorders.

Objectives: To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment. Study Design and Setting: In this systematic review, literature searches were conducted in Embase, Medline, and cumulative index of nursing and allied health literature from their inception until February 2024. Studies containing follow-up assessment (eg, prospective cohort studies, registry-based studies) investigating prediction models of outcomes (eg, pain intensity and disability) for people with LBP receiving conservative treatment were included. Two independent reviewers performed the study selection, the data extraction using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies, and risk of bias assessment using the Prediction model Risk of Bias Assessment. Findings of individual studies were reported narratively taking into account the discrimination and calibration measures of the prediction models. Results: Seventy-five studies developing or investigating the validity of 216 models were included in this review. Most prediction models investigated people receiving physiotherapy treatment and most models included sociodemographic variables, clinical features, and selfreported measures as predictors. The discriminatory capacity of the internal validity of the 27 prediction models for pain intensity varied greatly showing a c-statistic ranging from 0.48 to 0.94. Similarly, the discriminatory capacity for 31 models for disability had the same pattern showing a c-statistic ranging from 0.48 to 0.86. The calibration measures of the internal validity of the prediction models predicting pain intensity and disability showed to be adequate. Only one of 3 studies testing the external validity of models to predict pain intensity and disability and reported both discrimination and calibration measures, which showed to be inadequate. The prediction models predicting the secondary outcomes (eg, self-reported recovery, quality of life, return to work) showed varied performance measures for internal validity, and only 2 studies tested the external validity of models although they did not provide performance the performance measures. Conclusion: Several prediction models have been developed for people with LBP receiving conservative treatment; however, most show inadequate discriminatory validity. A few studies externally validated the prediction models and future studies should focus on testing this before implementing in clinical practice. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the

INTRODUCTION:Disability pension (DP) among young adults has steadily increased in Norway, with chronic pain and psychological distress among the main causes. This study examined the association between co-occurring psychological distress and chronic pain in adolescence and the risk of DP during emerging adulthood, before and after the Norwegian Labour and Welfare Administration (NAV) reform. Machine learning survival models were also developed to predict early DP and identify consistent risk factors across cohorts. METHODS:Data from two general population surveys in 1995-1997 (Young-HUNT1) and 2006-2008 (Young-HUNT3) were linked to public registry data for DP, with a mean follow-up of 12 years. These represent populations pre- and post-NAV reform, respectively. Crude and adjusted Cox proportional hazards models tested associations between chronic pain, psychological distress and their co-occurrence with early DP. Machine learning survival models were developed using common predictors across cohorts and validated temporally. SurvSHAP(t) and hazard ratios were used for explainable risk factor analysis. RESULTS:Co-occurring psychological distress and chronic pain were associated with increased early DP risk in Young-HUNT3 (HR = 2.25, [95 % CI 1.62 to 3.14]) but not in Young-HUNT1. Tree-based machine learning models showed strong predictive performance and generalizability over time, with ExtraSurvivalTrees achieving a validation c-index of 0.75 [95 % CI 0.74-0.76]. Consistent risk factors included low social integration, limited physical activity, and low parental education in both cohorts, while additional factors post-reform included low self-esteem, poor self-rated health, and negative school experiences. CONCLUSION:Adolescents with co-occurring psychological distress and chronic pain face an elevated risk of early DP. Early identification and support are crucial, as risk factors extend beyond medical conditions. Findings also suggest that NAV policy changes may have influenced DP receipt rates among young adults.

Lower back pain with or without radiating leg pain is a leading cause of disability worldwide. Several treatment options are available, and this article aims to understand better the decision-making involved in selecting appropriate treatments. A qualitative interview study was conducted with patients with lumbar spinal disorders and neurosurgeons specialising in spine surgery. Both groups of participants were asked to reflect on the decision-making process regarding whether to pursue surgery for back pain. The theoretical approach of distributed decision-making was applied. Results indicated that patients activated diverse information sources and considerations derived from their social networks when in the consultation room. Surgeons, on the other hand, were equipped with research-based knowledge and clinical practice experience. Effective communication was a shared concern for patients and surgeons during the actual decision-making. Factors such as patient diversity, the language used to discuss disease and illness, and the interpretation of risks played pivotal roles in the decision-making process. Regarding self-understanding, patients acted as agents for illness coping in their social networks. Surgeons recognised the imperative skill of facilitating rich patient dialogue as a crucial element in shared decision-making regarding potential surgical interventions. These findings demonstrate the importance of understanding decision-making as a distributed process where patients and clinicians are embedded in social networks and institutional contexts. In this process, patients must be recognised and engaged as individuals with diverse backgrounds and needs, especially during discussions focused on determining the most effective treatment approach for their specific cases.

OBJECTIVES:The objective was to develop, internally and externally validate a prognostic model for symptom dissatisfaction, assessed by the patient acceptable symptom state (PASS), for older adults (≥55 years) seeking primary care for a new episode of back pain. DESIGN:Development, internal and external validation of a prognostic model using data from two prospective cohort studies with a 1-year follow-up was conducted. PARTICIPANTS AND SETTING:The Norwegian cohort (n=452) was used for model development and internal validation. External validation was conducted using the Dutch cohort (n=675). OUTCOME MEASURES AND METHOD:The outcome was defined as symptom dissatisfaction based on the PASS at 12 months follow-up. A logistic regression model was developed using backward selection and internally validated using 200 bootstrap samples. External validation included recalibration of the model intercept and slope. Model performance was measured using Nagelkerke-R2, area under the curve (AUC) and calibration slope, calibration in-the-large (CITL) and calibration plots. RESULTS:At 12 months, ~55% reported dissatisfaction in both cohorts. The final model included disability, catastrophising, recent back pain episode, spinal rotation pain, baseline symptom satisfaction, symptom duration and recovery expectation as predictors. The internally validated model showed acceptable discrimination (AUC 0.75, 95% CI 0.71 to 0.78), R2 was 0.23, the calibration slope and CITL being 0.89 (95% CI 0.73 to 1.08) and 0.01 (95% CI -0.16 to 0.15), respectively. External validation performance after recalibration yielded AUC 0.68 (95% CI 0.65 to 0.70), slope 0.86 (95% CI 0.67 to 1.05) and CITL 0.08 (95% CI -0.01 to 0.16). CONCLUSIONS:This prognostic model could be a useful tool for predicting PASS outcomes among older adults with back pain. The external validation results imply that more research is needed to optimise predictions. TRIAL REGISTRATION NUMBER:NCT04261309.

Background and purpose: We aimed to externally validate machine learning models developed in Norway by evaluating their predictive outcome of disability and pain 12 months after lumbar disc herniation surgery in a Swedish and Danish cohort. Methods: Data was extracted for patients undergoing microdiscectomy or open discectomy for lumbar disc herniation in the NORspine, SweSpine and DaneSpine national registries. Outcome of interest was changes in Oswestry disability index (ODI) (≥ 22 points), Numeric Rating Scale (NRS) for back pain (≥ 2 points), and NRS for leg pain (≥ 4 points). Model performance was evaluated by discrimination (C-statistic), calibration, overall fit, and net benefit. Results: For the ODI model, the NORspine cohort included 22,529 patients, the SweSpine cohort included 10,129 patients, and DaneSpine 5,670 patients. The ODI model’s C-statistic varied between 0.76 and 0.81 and calibration slope point estimates varied between 0.84 and 0.99. The C-statistic for NRS back pain varied between 0.70 and 0.76, and calibration slopes varied between 0.79 and 1.03. The C-statistic for NRS leg pain varied between 0.71 and 0.74, and calibration slopes varied between 0.90 and 1.02. There was acceptable overall fit and calibration metrics with minor–modest but explainable heterogeneity observed in the calibration plots. Decision curve analyses displayed clear potential net benefit in treatment in accordance with the prediction models compared with treating all patients or none. Conclusion: Predictive performance of machine learning models for treatment success/non-success in disability and pain at 12 months post-surgery for lumbar disc herniation showed acceptable discrimination ability, calibration, overall fit, and net benefit reproducible in similar international contexts. Future clinical impact studies are required.