The SUPER reporting guideline suggested for reporting of surgical technique
Bibliographic record
Abstract
Background: Existing reporting guidelines pay insufficient attention to the detail and comprehensiveness reporting of surgical technique. The Surgical techniqUe rePorting chEcklist and standaRds (SUPER) aims to address this gap by defining reporting standards for surgical technique. The SUPER guideline intends to apply to articles that encompass surgical technique in any study design, surgical discipline, and stage of surgical innovation. Methods: Following the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network approach, 16 surgeons, journal editors, and methodologists reviewed existing reporting guidelines relating to surgical technique, reviewed papers from 15 top journals, and brainstormed to draft initial items for the SUPER. The initial items were revised through a three-round Delphi survey from 21 multidisciplinary Delphi panel experts from 13 countries and regions. The final SUPER items were formed after an online consensus meeting to resolve disagreements and a three-round wording refinement by all 16 SUPER working group members and five SUPER consultants. Results: The SUPER reporting guideline includes 22 items that are considered essential for good and informative surgical technique reporting. The items are divided into six sections: background, rationale, and objectives (items 1 to 5); preoperative preparations and requirements (items 6 to 9); surgical technique details (items 10 to 15); postoperative considerations and tasks (items 16 to 19); summary and prospect (items 20 and 21); and other information (item 22). Conclusions: The SUPER reporting guideline has the potential to guide detailed, comprehensive, and transparent surgical technique reporting for surgeons. It may also assist journal editors, peer reviewers, systematic reviewers, and guideline developers in the evaluation of surgical technique papers and help practitioners to better understand and reproduce surgical technique. Trial Registration: https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/#SUPER.
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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 | 0.340 | 0.625 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".