The STROCSS 2024 guideline: strengthening the reporting of cohort, cross-sectional, and case–control studies in surgery
Bibliographic record
Abstract
INTRODUCTION: First released in 2017, the STROCSS guidelines have become integral for promoting high-quality reporting of observational research in surgery. However, regular updates are essential to ensure they remain relevant and of value to surgeons. Building on the 2021 updates, the authors have developed the STROCSS 2024 guidelines. This timely revision aims to address residual reporting gaps, assimilate recent advances, and further strengthen observational study quality across all surgical disciplines. METHODS: A core steering committee compiled proposed changes to update the STROCSS 2021 guidelines based on identified gaps in prior iterations. An expert panel of surgical research leaders then evaluated the proposed changes for inclusion. A Delphi consensus exercise was used. Proposals that scored between 7-9 on a nine-point Likert agreement scale, by ≥70% of Delphi participants, were integrated into the STROCSS 2024 checklist. RESULTS: In total, 46 of 56 invited participants (82%) completed the Delphi survey and hence participated in the development of STROCSS 2024. All suggested amendments met the criteria for inclusion, indicating a high level of agreement among the Delphi group. All proposed items were therefore integrated into the final revised checklist. CONCLUSION: The authors present the updated STROCSS 2024 guidelines, which have been developed through expert consensus to further enhance the transparency and reporting quality of observational research in surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.594 | 0.766 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".