The SCARE 2018 statement: Updating consensus Surgical CAse REport (SCARE) guidelines
Bibliographic record
Abstract
INTRODUCTION: The SCARE Guidelines were published in 2016 to provide a structure for reporting surgical case reports. Since their publication, SCARE guidelines have been widely endorsed by authors, journal editors, and reviewers, and have helped to improve reporting transparency of case reports across a range of surgical specialties. In order to encourage further progress in reporting quality, the SCARE guidelines must themselves be kept up to date. We completed a Delphi consensus exercise to update the SCARE guidelines. METHODS: A Delphi consensus exercise was undertaken. All members of the previous Delphi group were invited to participate, in addition to researchers who have previously studied case reports, and editors from the International Journal of Surgery Case Reports. The expert group was sent an online questionnaire where they were asked to rate their agreement with proposed changes to each of the 24 items. RESULTS: 56 people agreed to participate and 45 (80%) invitees completed the survey which put forward modifications to the original guideline. The collated responses resulted in modifications. There was high agreement amongst the expert group. CONCLUSION: A modified and improved SCARE checklist is presented, after a Delphi consensus exercise was completed. The SCARE 2018 Statement: Updating Consensus Surgical CAse REport (SCARE) Guidelines.
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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 | 0.246 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".