Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason scoring system. The inherently subjective nature of the grading creates variability between pathologists, potentially resulting in suboptimal patient management decisions. These reproducibility challenges extend beyond Gleason scoring to encompass other critical diagnostic and prognostic markers, including cancer volume quantification and detection of cribriform morphology patterns and perineural invasion. Artificial intelligence (AI) applications in digital pathology have emerged as promising solutions for enhancing diagnostic consistency and accuracy, with recent research demonstrating that automated systems can match expert-level performance in prostate biopsy evaluation. Nevertheless, comprehensive validation studies have revealed concerning limitations in model generalisability when deployed across different clinical environments and patient populations. Recent systematic reviews revealed widespread risk-of-bias limitations and insufficient external validation in AI diagnostic studies, highlighting critical needs for accumulated evidence supporting generalisability before clinical implementation. Rigorous external validation with preregistered protocols using independent datasets from diverse clinical settings remains essential to establish the reliability and safety of AI-assisted prostate pathology systems.

This study protocol establishes a framework for the retrospective external validation of an AI system developed for prostate biopsy assessment, to be conducted on the case-control samples of the National Prostate Cancer Register of Sweden, ProMort study (1998-2015). The primary aim is to evaluate the AI model’s diagnostic accuracy and Gleason grading performance using completely independent datasets separate from any model development or previously used validation cohorts. The diversity of the validation samples, spanning multiple geographic regions, temporal collection periods and reference standards, allows evaluation of model robustness across varied clinical contexts. Secondary aims encompass evaluating AI performance in cancer length estimation and detection of cribriform patterns and perineural invasion. This protocol delineates procedures for data collection, reference standard clarification and prespecified statistical analyses, ensuring comprehensive validation and reliable performance assessment. The study design conforms to established reporting guidelines Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Standards for Reporting Diagnostic Accuracy Studies using Artificial Intelligence (STARD-AI), and recognised best practices for AI validation in medical imaging.

Data collection and usage were approved by the Swedish Regional Ethics Review Board and the Swedish Ethical Review Authority (permits 2012/1586-31/1, 2016/613-31/2, 2019-01395, 2019-05220). The study adheres to the Declaration of Helsinki principles, and findings will be made available in open access peer-reviewed publications.

The length and frequency of previous sickness absence (SA) spells have been shown to be associated with future SA. The aim was to examine if this pattern persisted during the COVID-19 pandemic among workers in retail and sales.

We used pseudonymised, individual-level data from three nationwide Swedish administrative registers to conduct a prospective cohort study.

Sweden.

All 306 933 blue-collar workers in retail and wholesale, aged 18–67 in Sweden in 2019.

Likelihood and length of SA.

We used a Negative Binomial Hurdle model to estimate incidence rate ratios (IRR) and odds ratios (ORs) to determine if SA patterns differed in 2020–2021 compared with 2018–2019. We examined how these patterns varied according to the length and frequency of SA in the preceding year. Only SA spells >14 days were included.

54 993 (18.5%) workers had SA during 2020–2021, an increase from 46 024 (15.6%) in 2018–2019. We observed a dose-response association between the number of prior SA days and the likelihood and length of future SA days, both before and during the pandemic. The likelihood of subsequent SA was higher in 2020–2021 compared with 2018–2019 among individuals with up to 180 prior SA days. Individuals with no prior SA had a lower average number of subsequent SA days during the pandemic (IRR (95% CI) 0.96 (0.94–0.98)) than in 2018–2019, while those with 1–30, 31–90 or 181–365 prior SA days had a higher average number of SA days during 2020–2021.

Individuals with many SA days prior to the pandemic were at particularly high risk of lengthy SA during the pandemic years.

Despite an increasing amount of research related to gender-affirming treatment (GAT) outcomes among transgender and gender-diverse (TGD) people (ie, people who experience discomfort or distress in the misalignment between their gender and sex assigned at birth) in recent years, the evidence base for current recommendations is suboptimal. One contributing factor is the heterogeneity in the outcomes and outcome measures used. This study seeks to address this challenge by developing a foundational core outcome set (COS) to be used for TGD adults receiving GAT in Sweden.

Recommendations from the Core Outcome Measures in Effectiveness Trials initiative will be used to address this aim in four phases. Phase 1, an umbrella review of peer-reviewed literature and international guidelines in GAT will be conducted to identify relevant outcomes. In phase 2, we will solicit input from TGD individuals through the review of patient and interest organisations’ reports and an anonymous survey to identify outcomes of personal significance. In phase 3, using the Delphi method, 2–3 rounds of assessment will be conducted where researchers, healthcare professionals, policy-makers and TGD adults rate the identified outcomes by perceived importance. In phase 4, a consensus meeting will convene representatives from all stakeholder groups to finalise the COS.

The results of this study will consist of a COS for GAT regarding TGD adults in Sweden. Participant survey responses will be evaluated using interpretive analysis to identify core outcomes. During each of the Delphi rounds, Likert-type scale ratings will be aggregated for outcomes to advance or be eliminated in each round.

The study has received ethical approval by the Swedish Ethical Review Authority (Umeå medicine department, Registration number: 2024-04672-01). The results of this study will be published open-access and disseminated through TGD interest organisations and a Swedish research network for gender dysphoria.

COMET registration number 3223.