Counties Manukau District Health Board

This study presents classification models trained to diagnose and grade prostate cancer using fresh prostate biopsies. We compare the performance of classification models with optimised sensitivity and specificity (standard models) with application-specific models designed to maximise sensitivity and negative predictive value (NPV). Standard models achieve 80% sensitivity and 81% specificity. Application-specific models, calibrated to 90% sensitivity and 95% NPV, are intended to provide clinicians with a tool they can use with confidence to support intraoperative decisions, specifically to improve tissue retention during biopsy procedures and to ensure clear surgical margins. To this end, we introduce a novel 5-layer algorithm that combines 5 application-specific models chosen for overall best performance. This algorithm can reduce the number of biopsy samples required for diagnosis by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity. All models are independently validated using two large patient cohorts. These results support the targeted use of Raman spectroscopy for real-time tissue analysis in diagnostic and intraoperative settings. The technology’s clinical value as a decision-support tool aligns with the shared goal of pathologists and urologists to reduce the number of prostate biopsy cores while maintaining high sensitivity for clinically significant cancer. Prior studies have improved biopsy efficiency, but their performance has been variable, and concerns remain regarding underdetection of significant disease, revealing the need for approaches that improve biopsy efficiency without increasing diagnostic risk. The technology described here provides a realistic solution for targeted biopsy guidance to support more precise and evidence-based clinical decisions.

This study presents classification models trained to diagnose and grade prostate cancer using fresh prostate biopsies. We compare the performance of classification models with optimised sensitivity and specificity (standard models) with application-specific models designed to maximise sensitivity and negative predictive value (NPV). Standard models achieve 80% sensitivity and 81% specificity. Application-specific models, calibrated to 90% sensitivity and 95% NPV, are intended to provide clinicians with a tool they can use with confidence to support intraoperative decisions, specifically to improve tissue retention during biopsy procedures and to ensure clear surgical margins. To this end, we introduce a 5-layer algorithm that combines 5 application-specific models chosen for overall best performance. This algorithm can reduce the number of biopsy samples required for diagnosis by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity. All models are independently validated using two large patient cohorts. These results support the targeted use of Raman spectroscopy for real-time tissue analysis in diagnostic and intraoperative settings. The technology’s clinical value as a decision-support tool aligns with the shared goal of pathologists and urologists to reduce the number of prostate biopsy cores while maintaining high sensitivity for clinically significant cancer. Prior studies have improved biopsy efficiency, but their performance has been variable, and concerns remain regarding underdetection of significant disease, revealing the need for approaches that improve biopsy efficiency without increasing diagnostic risk. The technology described here provides a realistic solution for targeted biopsy guidance to support more precise and evidence-based clinical decisions.

ObjectiveThe Paediatric Rheumatology International Trials Organisation (PRINTO) recently undertook an effort to better harmonize the pediatric and adult arthritis criteria. These provisional criteria are being refined for optimal performance. We aimed to investigate differences between patients who did and did not fulfill these PRINTO criteria among youth diagnosed with juvenile spondyloarthritis (SpA) that met axial juvenile SpA (axJSpA) classification criteria.MethodsThis was a retrospective cross-sectional sample of youth diagnosed with juvenile SpA who met the axJSpA classification criteria. Demographics, clinical manifestations, and physician and patient-reported outcomes were abstracted from medical records. Magnetic resonance imaging (MRI) scans underwent central imaging review by at least two central raters. Differences between groups were compared using Wilcoxon signed-rank test or chi-square test, as appropriate.ResultsOf 158 patients who met axJSpA criteria, 107 patients (68%) met the PRINTO provisional criteria for enthesitis/spondylitis-related arthritis. A total of 41 patients (26%) did not fulfill any of the three major PRINTO criteria due to lack of peripheral disease manifestations. Demographics, prevalence of inflammatory or structural lesions on MRI, family history of SpA, and duration of pain were not statistically different between those who did and did not meet PRINTO criteria. Those who fulfilled the PRINTO criteria had significantly more peripheral arthritis, enthesitis, and HLA-B27 positivity but reported less sacral/buttock pain.ConclusionPhenotypic differences of children with axJSpA between those who were and were not classified by the PRINTO criteria were primarily due to peripheral disease manifestations and HLA-B27 positivity. Modification of the PRINTO provisional criteria may facilitate capture of youth with primarily axial disease.

OBJECTIVES:To describe cricopharyngeal myectomy (CPMec) with Gold laser for the treatment of CP bar with Zenker's diverticulum and evaluate long-term outcomes of CPMec with Gold laser, utilizing quantitative fluoroscopic measures, dietary grading, and patient-reported metrics. METHODS:All patients undergoing CPMec with Gold laser over a 14-year period were evaluated. CPMec entails division of the diverticulum septum with removal of approximately 1 cm3 of muscle and mucosa. Demographic data, Eating Assessment Tool-10 scores, and videofluoroscopic swallow study (VFSS) parameters were compared pre- and postsurgery. RESULTS:Eighty-four patients underwent 90 successfully completed CPMec with Gold laser. EAT-10 scores decreased from mean of 20 to 2 (SD 9, p < 0.00) following surgery. Mean opening of the pharyngoesophageal segment (PESmax) improved from 0.56 to 0.86 cm (SD 0.4, p < 0.007), pharyngeal constriction ratio (PCR) improved from 0.15 to 0.09 (SD 0.1, p < 0.000) and bolus clearance ratio (BCR) improved from 16% residue to 4% (SD 6%, p < 0.000) following surgery. Six recurrences occurred (6.7%) and all were successfully treated with further CPMec (completion rate 90/92, 98%). Four (4.4%) post-operative leaks occurred, and all were managed conservatively. CONCLUSION:CPMec with Gold laser is safe, achievable, and provides significant symptomatic and objective improvement in swallowing for those with CP bar and Zenker's diverticulum. Removal of tissue reduces recurrence rates following CPMec and does not increase chances of adverse events. LEVEL OF EVIDENCE: 4:

Predictive modeling has advanced healthcare management by informing operational and strategic decisions; however, such models often exclude the fundamental care elements that shape patients' experiences, thereby limiting person-centered, quality-driven decisions. This gap is addressed by piloting a predictive model grounded in the Fundamentals of Care (FoC) Framework, which captures how system-level and policy-level conditions (Context of Care) influence care outcomes (Integration of Care) through the clinician–patient Relationship. A cross-sectional, exploratory design was implemented across Spain (hospital-based care, n = 55) and Australia (community-based care, n = 32). A culturally adapted, 41-item patient-reported experience measure was developed to operationalize the FoC Framework’s three core dimensions. Structural relationships were tested using Consistent Partial Least Squares Structural Equation Modeling to assess model feasibility. Results indicate that the Context of Care significantly predicted Relationship in both countries (Spain: β = 0.33; Australia: β = 0.68), which strongly predicted Integration of Care (Spain: β = 0.84; Australia: β = 0.92), fully mediating the effect of Context, which had no direct influence on Integration. Findings highlight a consistent structural pathway: Context of Care → Relationship → Integration of Care. This pilot demonstrates the feasibility of integrating patient-reported experience data into predictive modeling grounded in fundamental care delivery theory, offering transferable insights into how system- and policy-level conditions shape care quality through the Relationship dimension. The model supports early-stage decision-making tools for quality improvement, workforce planning, and system design, emphasizing the importance of a Context of Care that enables clinician-patient Relationships to achieve effective Integration of Care.