Reporting guidelines for surgical technique could be improved: a scoping review and a call for action
Bibliographic record
Abstract
OBJECTIVES: To identify reporting guidelines related to surgical technique and propose recommendations for areas that require improvement. STUDY DESIGN AND SETTING: A protocol-guided scoping review was conducted. A literature search of MEDLINE, the EQUATOR Network Library, Google Scholar, and Networked Digital Library of Theses and Dissertations was conducted to identify surgical technique reporting guidelines published up to December 31, 2021. RESULTS: We finally included 55 surgical technique reporting guidelines, vascular surgery (n = 18, 32.7%) was the most common among the clinical specialties covered. The included guidelines generally showed a low degree of international and multidisciplinary cooperation. Few guidelines provided a detailed development process (n = 14, 25.5%), conducted a systematic literature review (n = 13, 23.6%), used the Delphi method (n = 4, 7.3%), or described post-publication strategy (n = 6, 10.9%). The vast majority guidelines focused on the reporting of intraoperative period (n = 50, 90.9%). However, of the guidelines requiring detailed descriptions of surgical technique methodology (n = 43, 78.2%), most failed to provide guidance on what constitutes an adequate description. CONCLUSION: Our study demonstrates significant deficiencies in the development methodology and practicality of reporting guidelines for surgical technique. A standardized reporting guideline that is developed rigorously and focuses on details of surgical technique may serve as a necessary impetus for change.
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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 | 0.688 | 0.809 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.027 | 0.051 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".