Developing the surgical technique reporting checklist and standards: a study protocol
Bibliographic record
Abstract
BACKGROUND: Standardized and transparent reporting of surgical technique is the cornerstone of effective dissemination, implementation and improvement. However, current reporting of surgical techniques is inadequate. The existing guidelines potentially applied to guide surgical technique reporting are with a minimal highlight of the surgical technique, lack requirements explaining what extent and dimensions need to be described in detail, or are unlikely to extrapolate to a wide range of surgical techniques. This study aims to formulate a rigorous protocol to develop a surgical technique reporting checklist and standards (SUPER) that defines what a clear, comprehensive and detailed surgical technique report should be contained. METHODS: This protocol is designed following the classic guidance for developing reporting guidelines recommended by the EQUATOR network. RESULTS: The development team will consist of surgeons (~80%), methodologists, and journal editors. The draft checklist sources will include a scoping review of existing reporting guidelines related to surgical technique, surgical technique articles from 15 top journals published in the last year, and brainstorming by the multidisciplinary development team. The final SUPER checklist will be formed after three rounds of Delphi surveys, one round of face-to-face meeting, and a month-long pilot test. The SUPER checklist will be published as open-access and be used in combination with existing reporting guidelines related to surgical techniques (e.g., IDEAL). This protocol will steer the SUPER checklist's development, allowing us to further elaborate surgical technique reporting for all surgical specialties, and enabling a more favorable experience for surgeons, nurses, medical students, residents, editors, and reviewers. TRIAL REGISTRATION: This trial is registered at the EQUATOR network on December 18th, 2020. Available at: https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.324 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; both teacher heads 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".