The SCARE 2020 Guideline: Updating Consensus Surgical CAse REport (SCARE) Guidelines
Bibliographic record
Abstract
INTRODUCTION: The SCARE Guidelines were first published in 2016 and were last updated in 2018. They provide a structure for reporting surgical case reports and are used and endorsed by authors, journal editors and reviewers, in order to increase robustness and transparency in reporting surgical cases. They must be kept up to date in order to drive forwards reporting quality. As such, we have updated these guidelines via a DELPHI consensus exercise. METHODS: The updated guidelines were produced via a DELPHI consensus exercise. Members were invited from the previous DELPHI group, as well as editorial board members and peer reviewers of the International Journal of Surgery Case Reports. The expert group completed an online survey to indicate their agreement with proposed changes to the checklist items. RESULTS: A total of 54 surgical experts agreed to participate and 53 (98%) completed the survey. The responses and suggested modifications were incorporated into the new 2020 guideline. There was a high degree of agreement amongst the SCARE Group, with all modified SCARE items receiving over 70% scores 7-9. CONCLUSION: A DELPHI consensus exercise was completed and an updated and improved SCARE Checklist is now presented.
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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 | 0.106 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".