BACKGROUND:The Lancet Commission on Global Surgery (LCoGS) defined six indicators with 2030 targets to track national surgical system performance. The aim of this systematic review was to evaluate national reporting and attainment of benchmarks for each indicator and to assess the quality of modelling studies used to fill data gaps. METHODS:Seven bibliographic databases (1 April 2015-24 July 2024) and government domains of 48 countries committed to National Surgical, Obstetric, and Anaesthesia Plans were searched. Records providing national estimates of any LCoGS indicator were eligible. The primary outcome was the proportion of World Bank-classified countries meeting indicator benchmarks and the secondary outcome was the quality of modelled national estimates. This systematic review was prospectively registered in PROSPERO, the international prospective register of systematic reviews (CRD420250650890). RESULTS:Of 4245 records retrieved, 44 studies were included (35 research articles and 9 policy documents). Among 217 World Bank-classified countries, access to timely essential surgery (indicator 1) was reported for 94 countries (39% meeting benchmark), specialist surgical workforce density (indicator 2) was reported for 167 countries (50.3% meeting benchmark), surgical volume (indicator 3) was reported for 124 countries (31.5% meeting benchmark), perioperative mortality (indicator 4) was reported for 74 countries (no benchmark was set at country level), and financial risk protection indicators (indicators 5 and 6) were reported for five countries, with none meeting either benchmark. Across indicators, high-income countries were more likely to meet benchmarks. Most modelled studies lacked transparency in data sources, statistical methods, or model validation. CONCLUSION:Reporting of LCoGS indicators remains sparse and uneven, particularly in low- and middle-income countries. Without standardized, routine measurement and minimum quality standards for modelled estimates, progress towards 2030 cannot be credibly tracked. Integrating surgical metrics into national health information systems should be a policy priority.

Regression models provide information on complex relationships between patient factors, investigations, diagnoses, treatments, and outcomes. These inferences underpin evidence-based medicine. However, by default regression models assume straight-line relationships, and the common approach of splitting continuous variables into groups has several disadvantages. We discuss pitfalls with these common approaches, and provide an interactive regression model playground, which acts as a point-and-click showcase of these concepts. More flexible regression modelling techniques are available, which allow non-linear relationships between predictors and outcome to be captured. However, they have been shown to be underused in medical research. We feel a major contributor to this is that more flexible non-linear models are typically explained for a statistical audience, creating a barrier for medical professionals. In this article, we introduce non-linear regression for medical researchers. Specifically, we focus on restricted cubic splines (RCS), which allow curved relationships to be fit, within the context of regression models. This has the benefit that the overall structure of the regression model and its outputs, which are familiar to medical researchers, stays the same, with the simple addition of non-linear modelling of specific variables. We implement RCS in a case study, with accompanying example R scripts (available on GitHub). We also launch an R package ("rmsMD") which aims to make this technique approachable to medical researchers, as well as creating publication-ready tables and plots. Overall, this article equips medical researchers with an intuitive understanding of non-linear modelling, which can then be applied with the easy-to-use tools provided.

BACKGROUND:Preexisting multiple (two or more) long-term conditions (MLTCs) may negatively affect recovery after COVID-19. We investigated how preexisting MLTCs, including different categorization and patterns of MLTCs, affect 1-year health outcomes after severe COVID-19. METHODS:Adults post-hospitalization after COVID-19 were recruited during 2020-2021. We compared recovery at 1 year after discharge using adjusted multivariable logistic regression in 1:1 propensity-matched adults (for age, sex, ethnicity, social deprivation, obesity, and smoking history) with and without preexisting MLTCs. In adults with MLTCs, different categorization such as number of conditions, number and types of body systems involved (e.g. respiratory, cardiovascular), and latent class analysis-derived patterns of condition co-occurrence were assessed for their association with recovery at 1 year. RESULTS:A total of 647 adults with MLTCs were matched with 647 adults without MLTCs (n = 1294; 61.9% male, 79.6% of White ethnicity, median age 59 [interquartile range 52-67] years). The presence of MLTCs was associated with lower odds of feeling fully recovered (odds ratio 0.66 [95% confidence interval 0.51-0.85], P = 0.001). In those with MLTCs, recovery was negatively affected by number and type of body systems involved (e.g. respiratory [odds ratio 0.49 (95% confidence interval 0.34-0.69), P <0.001]) but not by the number of conditions (P >0.1). Four latent classes of MLTC co-occurrence were estimated with different risks of recovery (P <0.01). CONCLUSION:Adults with preexisting MLTCs were 34% less likely to feel fully recovered at 1 year after COVID-19 hospitalization than adults without MLTCs. We describe prognostic classifications of MLTCs, with future work needed to understand whether they have prognostication in broader post-acute infection sequalae.

Background: While maintenance rituximab therapy (MT) for follicular lymphoma improves progression-free survival, individual benefit varies, creating a critical need to avoid overtreatment and its associated burdens. Methods: We developed a double machine learning framework using a retrospective 404-patient cohort to estimate the individualized treatment effect of MT on the risk of disease progression within 24 months. Results: Our model revealed heterogeneity in treatment benefit, successfully distinguishing patients most likely to benefit from those who are not. A retrospective simulated application identified a “low-risk, low-benefit” subgroup that might potentially forgo MT. Furthermore, aligning historical clinical decisions with our model’s recommendations was associated with a lower progression rate compared to nonalignment (14.8% vs. 38.0%). Extensive sensitivity analyses confirmed the robustness of this treatment heterogeneity against potential unmeasured confounding and temporal practice shifts. Conclusions: Supported by an updated interactive clinical decision tool, this data-driven framework provides a robust strategy to personalize MT by separating prognostic risk from predictive benefit. It serves as a hypothesis-generating template to guide future prospective risk-adapted trials and reduce unnecessary treatment in oncology.

Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to be inferior to specialized models, creating potential for misuse and misunderstanding. To address this, our ClinicRealm benchmark systematically evaluates 15 GPT-style LLMs, 5 BERT-style models, and 11 traditional methods on unstructured clinical notes and structured Electronic Health Records (EHR) across predictive performance, reasoning, fairness, etc. Our findings reveal a significant shift: on clinical notes, leading zero-shot LLMs (e.g., DeepSeek-V3.1-Think, GPT-5) now decisively outperform finetuned BERT models. On structured EHRs, while specialized models excel with ample data, advanced LLMs demonstrate potent zero-shot capabilities, often surpassing conventional models in data-scarce settings. Notably, leading open-source LLMs match or exceed their proprietary counterparts. This provides compelling evidence that modern LLMs are competitive tools for clinical prediction, necessitating a re-evaluation of model selection strategies by health data scientists and developers.

BACKGROUND:More than 5 million surgical procedures are performed every year in the UK, but little is known about individuals at high surgical risk who account for most deaths after surgery. We aimed to describe the size, demographics, and long-term outcomes of high-risk surgical patients in the UK. METHODS:In this retrospective, observational cohort study, we analysed routine National Health Service (NHS) data from England (hospital episode statistics, and admitted patient care), Scotland (Scottish Morbidity Records), and Wales (Patient Episode Database for Wales). We derived death data from linkage to Office for National Statistics (England & Wales), National Records of Scotland (Scotland), and the Welsh Death Service (Wales). We included adults (aged ≥18 years) undergoing elective or emergency, in-patient or day-case, non-obstetric surgery between Jan 1, 2015, and Dec 31, 2019 (Dec 31, 2018, in Wales) using an existing code-based definition. We included patients in sequential surgical spells (from day of surgery to 365 days follow-up) and patients who underwent more than one surgical procedure over the study period could contribute more than one surgical spell. The primary (explanatory) outcome was death within 90 days after the index surgery. Secondary outcomes were duration of hospital stay, 90-day emergency hospital readmission, 1-year mortality, and 5-year survival. Patients were ranked from highest to lowest risk of 90-day death using a logistic regression model. The high-risk group included patients accounting for the first 80% of deaths, moderate-risk group included those accounting for the next 15% of deaths, and the low-risk group included those accounting for the final 5% of deaths. We compared high-risk and low-risk groups with risk ratios (RR; 95% CI). FINDINGS:We included 12 905 737 patients (mean age 57·5 [SD 19·2] years; 8 584 593 [53·2%] women, 7 552 101 [46·8%] men; 12 086 325 [74·9%] White patients) who experienced 16 136 694 surgical spells. There were 262 500 (1·6%) deaths within 90 days, and 644 596 (4·0%) deaths within 1 year. The median length of stay was 0 days (IQR 0-1), and there were 1 506 299 (9·4%) emergency readmissions within 90 days after surgery. We identified 1 481 357 (9·2%) patient spells in the high-risk group, who had experienced 206 257 (13·9%) deaths within 90 days (RR vs low 117·15 [115·18-119·14]), a median length of stay of 10 days (IQR 3-21), 426 496 (30·9%) emergency readmissions (RR vs low 5·67 [5·65-5·69]), and 402 033 (27·1%) deaths within 1 year (RR vs low 38·89 [38·61-39·17]). As a proportion of all outcomes in the cohort, high-risk patients accounted for 53·8% of all hospital bed-days used after surgery (21 206 482 of 39 441 408), 28·3% of emergency readmissions (426 496 of 1 506 299), and 62·4% of 1-year deaths (402 033 of 644 596). In England, 94·9% (95% CI 94·8-94·9) of patients in the low-risk group survived by 5 years, compared with 75·1% (75·0-75·2) of patients in the moderate-risk group, and 40·3% (40·1-40·4) of patients in the high-risk group. INTERPRETATION:Data from 2015-19 suggest that around 300 000 high-risk patients undergo surgery each year in the NHS. This group have poor outcomes and very high health-care resource needs, which presents important health policy and delivery challenges in the UK. FUNDING:National Institute for Health and Care Research (NIHR).

BACKGROUND:Major liver surgery is associated with significant physiological stress and a high rate of postoperative complications. Prehabilitation aims to enhance physiological reserve before surgery. Despite the growing volume of liver resections worldwide, the impact of prehabilitation on clinical and economic outcomes in patients undergoing elective liver resection is poorly understood. METHODS:A systematic review and meta-analysis was reported in accordance with PRISMA guidelines. MEDLINE, Embase, Web of Science and the Cochrane Library were searched in November 2025. Randomized controlled trials and comparative observational studies evaluating preoperative programs with a structured exercise component, with or without additional nutritional and psychological support in adult patients undergoing major elective liver surgery were included. RESULTS:Six studies (four RCTs and two comparative cohort studies) comprising 557 patients were included. Prehabilitation resulted in significantly fewer overall postoperative complications (OR 0.55; 95% CI, 0.37 to 0.84; p = 0.005). No significant differences were observed for length of stay (MD -0.38 days; 95% CI, -1.08 to 0.32; p = 0.29), major complications (OR 0.79; 95% CI, 0.50 to 1.27; p = 0.33), mortality (OR 0.40; 95% CI, 0.05 to 3.24; p = 0.39), readmission (OR 0.85; 95% CI, 0.47 to 1.55; p = 0.60) and hospitalization costs (MD = -137.13; 95% CI, -642.19, 367.93; p = 0.59). CONCLUSION:Prehabilitation significantly reduces overall postoperative complications following liver resection. The absence of standardized, liver-specific interventions limits the determination of effective program design. Future research should prioritize standardised protocols and evaluate post-hepatectomy functional recovery, patient-reported outcomes and cost-effectiveness.

During the COVID-19 pandemic, prioritization of COVID-19 patients led to delays in oncological surgery, potentially impacting patient outcomes. This analysis examines the effects of surgical delays in various tumor entities on resectability and postoperative mortality. Data from the COVIDSurg Cancer Collaborative, an international prospective cohort study with 19,676 patients, collected between March 26, 2020, and September 16, 2020, were analyzed. Postoperative mortality and complete resection (R0) were the outcomes, with tumor entity, stage and delay to surgery as key exposures. 17,486 patients underwent surgery during the study period, at a median time of three weeks after decision to operate (IQR = 4). 172 (1.0

Introduction Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in males in the UK. Of the 55 000 new cases each year, up to 50% of these patients will receive radiotherapy either alone or after prostatectomy. Although there have been significant improvements in the accuracy of radiotherapy delivery leading to better tumour targeting and a reduction in dose to normal tissues, significant permanent genito-urinary or gastrointestinal-related side effects are all too common. With nearly 80% of patients with prostate cancer surviving for 10 years or more, minimising life-limiting radiation damage to normal tissues is vitally important. However, at present, it is not possible to identify which patients will suffer a poorer outcome after radiotherapy. The aim of this study, improving radiotherapy in PROState cancer using EleCtronic population-based healthCAre data (PROSECCA), is to do this by using the existing information in a patient’s digital healthcare record. By linking primary, secondary and tertiary clinical data, including digital image information, with radiotherapy treatment plans and outcome data, the PROSECCA study will identify de novo predictive biomarkers of radiation response and provide clinicians with a tool to individualise a radiotherapy dose and plan to maximise cure and minimise toxicity.Methods and analysis The PROSECCA study is a large multidisciplinary project, the purpose of which is to analyse healthcare records from up to 15 000 patients with prostate cancer who underwent radiotherapy in the treatment of their cancer in Scotland between 2010 and 2022. Through the linkage of data obtained specifically for radiotherapy and data held within each patient’s unique electronic health record (EHR), the factors that indicate why some patients have a poor response to treatment, or an increased risk of side effects from radiation, will be identified. This will be made possible by the use of artificial intelligence and machine learning (AL/ML), which will help to identify at-risk patients earlier and allow adaptation of their treatment accordingly.Ethics and dissemination The study is being conducted in accordance with the ethical principles set out in the Declaration of Helsinki and Good Clinical Practice that respects and protects the rights, and maintains confidentiality, of all trial participants. The study protocol (V.1.0) was reviewed by the South Central Oxford A Research Ethics Committee (REC) on 13 December 2021 and received a favourable opinion subject to each National Health Service (NHS) organisation confirming permission for patients treated within their area. Approval for the use of unconsented healthcare record data for patients included in the study and treated at one of the five Scottish Cancer Centres required an application to the NHS Scotland Public Benefit and Privacy Panel for Health and Social Care (HSC-PBPP). Full approval from the HSC-PBPP panel was received on 1 July 2024, which covered the use of pseudoanonymised EHR data for all patients participating in the study. The study is publicly listed on the NHS Health Research Authority site, with IRAS ID 306245 and REC reference 21/SC/0402. Dissemination of the study findings will take place through field-leading cancer, radiation oncology and medical physics journals. All manuscripts will be approved by the main study team and authorship determined by mutual agreement.Trial registration number NCT06714630.

Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when tens or even hundreds of features require selection, transformation, or interaction modelling. While complex machine learning models offer high performance, their "black-box" nature limits the clinical trust, transparency, and interpretability required for decision-making. We developed and evaluated an Exploratory AI Recommender that provides data-driven recommendations to improve predictive performance of existing interpretable statistical models. The developed framework uses flexible AI modelling to capture complex data patterns and explainable AI techniques to translate the patterns into three recommendation types: feature exclusion, non-linear terms, and feature interactions. We evaluated the framework by comparing predictive performance of a baseline (i.e., no interactions or non-linear terms) Cox Proportional Hazards (CPH) model against an augmented CPH incorporating recommendations suggested by our method. The primary analysis predicts the time to the first occurrence of a fall or related injury in 245,614 patients. Our method recommended excluding 23 features, including non-linear terms for two features, and including 221 suggested feature interactions. The C-index improved from 0.805 (95

BACKGROUND:The primary aim of this study was to provide contemporary, real-world data on the management approaches and survival outcomes of patients with incidental gallbladder cancer (GBC) following cholecystectomy in the UK. The secondary aim was to identify prognostic factors associated with survival. METHODS:Patients diagnosed with incidental GBC following cholecystectomy between January 2014 and December 2022 across 24 centres were included. Data collected comprised demographics, treatment details, histopathological findings, and survival outcomes. RESULTS:During the study interval, 285 patients had incidental GBC. The median follow-up was 31 months, with 5-year disease-free survival (DFS) and overall survival (OS) of 41.5% and 45.1% respectively. Of the 193 patients (67.7%) who underwent liver resection, most (97.9%) underwent segment 4B/5 resection. Patients with incidental GBC who underwent liver resection had significantly improved DFS (51 versus 15 months, P < 0.001) and OS (72 versus 26 months, P < 0.001) compared with those who did not. In addition, patients who completed adjuvant chemotherapy had better DFS (35 versus 15 months, P = 0.021) and OS (47 versus 26 months, P = 0.009) compared with those who did not. On multivariable analysis, nodal metastases were independently associated with poorer DFS (HR 2.04 (95% c.i. 1.30 to 3.20), P = 0.002), while advanced tumour (T3-T4) stage (HR 1.70 (95% c.i. 1.04 to 2.77), P = 0.034) and nodal metastases (HR 2.15 (95% c.i. 1.33 to 3.48), P = 0.002) predicted poorer OS. CONCLUSION:Patients who underwent liver resection after incidental GBC had significantly better survival than those who did not proceed to further surgery. Adverse tumour biology was associated with poorer survival.

Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare's reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI's logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather than replace clinical judgment.

Ductular reactions (DRs) are dynamic and complex multicellular responses that occur as a result of various hepatic injuries. Precise identification and quantification of the extent of DRs is a cornerstone of pre-clinical modelling of liver disease, with links to inflammation, fibrosis, regeneration, and disease severity. Here, we apply a deep learning model, Deep Understanding Convolutional Kernel (DUCK-Net), to the automated detection and segmentation of DRs in whole-slide histopathological images of murine models of liver damage. Following annotation of a training dataset by a specialist liver histopathologist, we demonstrate accelerated performance and accurate detection, achieving a mean Dice coefficient (model-expert segmentation overlap) of 85.4% and a specificity of 98%, indicating minimal false positives. Evaluation of model validity and utility was achieved with a histological time course of cholestatic injury and recovery using 3,5-diethoxycarbonyl-1,4-dihydrocollidine diet (DDC) in mice. When assessed against a multiple linear regression model incorporating core epithelial and stromal components of the DR as quantified using immunohistochemistry (IHC), DUCK-Net predicted the spatiotemporal response to injury and repair/resolution with a coefficient of determination (R2) of 0.88. Moreover, DUCK-Net kinetics strongly correlated with published spatial transcriptomic (Stereo-seq) analysis of the DDC model, demonstrating that H&E-based segmentation captured molecular DR dynamics comparable to or exceeding that of individual IHC markers without the need for immunostaining. DUCK-Net provides a novel and accessible platform for rapid, accurate histological quantification of liver injury reflective of the matrix-rich, multicellular regenerative niche observed in DRs. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

INTRODUCTION:Peri-operative allogeneic red blood cell transfusion is hypothesised to increase the risk of cancer recurrence following cancer surgery. However, previous data supporting this association are limited by residual confounding. We conducted an umbrella review (i.e. a systematic review of systematic reviews) to synthesise and evaluate the evidence between red blood cell transfusion and cancer recurrence. METHODS:We searched online databases for systematic reviews of red blood cell transfusion and cancer-related outcomes. The AMSTAR 2 tool was used for quality assessment. The adequacy of confounding adjustment was judged according to a consensus-derived framework. RESULTS:We included five relevant systematic views which included patient populations ranging from 2110 to 184,190. Two reviews reported cancer recurrence, and all reported an association with red blood cell transfusion. Three reviews reported positive associations between red blood cell transfusion and adverse outcomes including all-cause mortality, recurrence-free survival and cancer-related mortality. According to AMSTAR 2, four reviews were rated as 'critically low quality' and one as 'low quality'. There was variation in how systematic reviews assessed the risk of bias from confounding. Compared with our pre-derived framework, we found a high likelihood of unmeasured confounding. DISCUSSION:Currently available evidence describes an association between peri-operative red blood cell transfusion and cancer recurrence, but this is mostly of low to critically low quality, with minimal control for residual confounding. Further research, at low risk of bias, is required to provide definitive evidence and inform practice.

OBJECTIVE:Post-COVID syndrome involves prolonged symptoms with multisystem and functional impairment lasting ≥12 weeks after acute coronavirus disease 2019 (COVID-19). We aimed to determine the efficacy of exercise-based rehabilitation interventions, either face-to-face or remote, compared to usual care in individuals experiencing post-COVID syndrome following a hospitalisation with acute COVID-19. DESIGN:This single-blind randomised controlled trial compared two exercise-based rehabilitation interventions (face-to-face or remote) to usual care in participants with post-COVID syndrome following a hospitalisation. The interventions were either a face-to-face or remote 8-week programme of individually prescribed exercise and education. The primary outcome was the change in Incremental Shuttle Walking Test (ISWT) following 8 weeks of intervention (either face-to-face or remote) compared to usual care. Other secondary outcomes were measured including health-related quality of life (HRQoL), and exploratory outcomes included lymphocyte immunotyping. RESULTS:181 participants (55% male, mean±sd age 59±12 years, length of hospital stay 12±19 days) were randomised. There was an improvement in the ISWT distance following face-to-face rehabilitation (mean 52 m, 95% CI 19-85 m; p=0.002) and remote rehabilitation (mean 34 m, 95% CI 1-66 m; p=0.047) compared to usual care alone. There were no differences between groups for HRQoL self-reported symptoms. Analysis of immune markers revealed significant increases in naïve and memory CD8+ T-cells following face-to-face rehabilitation versus usual care alone (p<0.001, n=31). CONCLUSION:Exercise-based rehabilitation improved short-term exercise capacity in post-COVID syndrome following an acute hospitalisation and showed potential for beneficial immunomodulatory effects.

Background Long covid has emerged as a complex health condition for millions of people worldwide following the COVID-19 pandemic. Previously, we have categorised healthcare pathways for patients after discharge from hospital with COVID-19 across 45 UK sites. The aim of this work was to estimate the clinical and cost-effectiveness of these pathways.Methods We examined prospectively collected data from 1013 patients at 12 months postdischarge on whether they felt fully recovered (self-report), number of newly diagnosed conditions (NDC), quality of life (EuroQoL-five dimension-five level (EQ-5D-5L) utility score compared with pre-COVID estimate) and healthcare resource costs (healthcare records). An analysis of the cost-effectiveness was performed by combining the healthcare resource cost and 1-year EQ-5D (giving a quality-adjusted life-year (QALY)) using statistical models that accounted for observed confounding.Results At 1 year, 29% of participants felt fully recovered, and 41% of patients had an NDC. The most comprehensive services, where all patients could potentially access assessment, rehabilitation and mental health services, were more clinically effective when compared with either no service or light touch services (mean (SE) QALY 0.789 (0.012) vs 0.725 (0.026)), with an estimated cost per QALY of £1700 (95% uncertainty interval: dominated to £24 800).Conclusion Our analysis supports the need for proactive, stratified, comprehensive follow-up, particularly assessment and rehabilitation for adults after hospitalisation with COVID-19, showing these services are likely to be both clinically and cost-effective according to commonly accepted thresholds.

Opioids are frequently overprescribed after surgery. We applied a tabular foundation model to predict the risk of post-discharge opioid consumption. The model was trained and internally validated on an 80:20 training/test split of the ‘Opioid PrEscRiptions and usage After Surgery’ (ACTRN12621001451897p) study cohort, including adult patients undergoing general, orthopaedic, gynaecological and urological operations (n = 4267), with external validation in a distinct cohort of patients discharged after general surgical procedures (n = 826). The area under the receiver operator curve was 0.84 (95% confidence interval [CI] 0.81–0.88) at internal testing and 0.77 (95% CI 0.74–0.80) at external validation. Brier scores were 0.13 (95% CI 0.12–0.14) and 0.19 (95% CI 0.17–0.2). Patients with a <50% predicted risk of opioid consumption consumed a median of 0 oral morphine equivalents in the first week after surgery. Applying this model would reduce opioid prescriptions by 4.5% globally, and counterfactual modelling suggests without increasing time in severe pain (−4.3%, 95% CI −17.7 to 8.6).

Progress towards The Lancet Commission on Global Surgery's 2030 targets has been too slow and too patchy, particularly in low-income and middle-income countries. The unmet need for surgery has continued to grow, reaching at least 160 million operations per year. Ensuring high-quality surgical care remains a crucial global challenge, with 3·5 million adults dying after surgery each year. The COVID-19 pandemic exposed the fragility of surgical services long undermined by chronic underfunding, workforce shortages, and under-resourced infrastructure. However, The Lancet Commission on Global Surgery inspired a new generation of surgeons to engage with policy, and several countries have developed national surgical plans, although most remain unfunded. Advancements in surgical data science have allowed health systems to identify priorities for improvement. Preserving this infrastructure is important, especially during periods of uncertain global health funding. The next decade requires urgent change to prevent economic instability and armed conflict from forcing surgery down the global health agenda. Reframing surgery as an essential service that saves lives, strengthens health systems, and fosters economic productivity could unlock much needed investment. Sustained progress requires integration of funding both within hospital infrastructure and across care pathways. Such holistic approaches would reinforce entire hospital systems, which are essential to national security and wellbeing.

BACKGROUND:Cholecystectomy is a common procedure with a notable risk of iatrogenic bile duct injury. Understanding the factors contributing to bile duct injury and the effectiveness of preventative measures is crucial for improving surgical outcomes. This meta-analysis aimed to identify and synthesize high-quality evidence on risk factors and mitigating measures associated with bile duct injury after cholecystectomy. METHODS:Following the PRISMA guidelines, a comprehensive literature search was conducted across multiple databases. Included studies reported on adult patients undergoing cholecystectomy with relevant risk factors for bile duct injury. Meta-analyses of unadjusted and adjusted risk estimates were conducted with a random-effects model to account for heterogeneity. The study period across all included studies spanned from 1989 to 2016. RESULTS:The review included 31 studies comprising 6 513 599 cholecystectomies and 18 259 bile duct injuries. The primary risk factors identified were male sex (adjusted odds ratio 1.27, 95% confidence interval 1.13 to 1.39) and acute cholecystitis (adjusted odds ratio 1.74, 1.27 to 2.39). The critical view of safety was inconsistently documented and not statistically linked to reduced bile duct injury. Intraoperative cholangiogram's routine use did not show a statistically significant association with reduced incidence of bile duct injury (adjusted odds ratio 0.92, 0.70 to 1.23). CONCLUSION:Male sex and acute cholecystitis significantly increase the risk of bile duct injury after cholecystectomy. Risk stratification for these patients before surgery would ultimately aid the shared decision-making consent process.