The SUPER reporting guideline suggested for reporting of surgical technique: explanation and elaboration
Bibliographic record
Abstract
Background: Surgical technique plays an essential role in achieving good health outcomes. However, the quality of surgical technique reporting remains heterogeneous. Reporting checklists could help authors to describe the surgical technique more transparently and effectively, as well as to assist reviewers and editors evaluate it more informatively, and promote readers to better understand the technique. We previously developed SUPER (surgical technique reporting checklist and standards) to assist authors in reporting their research that contains surgical technique more transparently. However, further explanation and elaboration of each item are needed for better understanding and reporting practice. Methods: We searched surgical literature in PubMed, Google Scholar and journal websites published up to January 2023 to find multidiscipline examples in various article types for each SUPER item. Results: We explain the 22 items of the SUPER and provide rationales item by item alongside. We provide 69 examples from 53 literature that present optimal reporting of the 22 items. Article types of examples include pure surgical technique, and case reports, observational studies and clinical trials that contain surgical technique. Examples are multidisciplinary, including general surgery, orthopaedical surgery, cardiac surgery, thoracic surgery, gastrointestinal surgery, neurological surgery, oncogenic surgery, and emergency surgery etc. Conclusions: Along with SUPER article, this explanation and elaboration file can promote deeper understanding on the SUPER items. We hope that the article could further guide surgeons and researchers in reporting, and assist editors and peer reviewers in reviewing manuscripts related to surgical technique.
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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 | 0.378 | 0.300 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".