CORR Insights®: Is the Addition of Anaerobic Coverage to Perioperative Antibiotic Prophylaxis During Soft Tissue Sarcoma Resection Associated With a Reduction in the Proportion of Wound Complications?
Bibliographic record
Abstract
1Clinical Professor, Department of Orthopaedic Surgery, University of Montreal, Montreal, Quebec, Canada M. Isler ✉, University of Montreal, Hopital Maisonneuve-Rosemont, Orthopedic Oncology Unit, 5345 Boulevard de l'Assomption, Montreal, Quebec H1T 4B3, Canada, Email: [email protected] This CORR Insights® is a commentary on the article "Is the Addition of Anaerobic Coverage to Perioperative Antibiotic Prophylaxis During Soft Tissue Sarcoma Resection Associated With a Reduction in the Proportion of Wound Complications?" by Ramsey and colleagues available at: DOI: 10.1097/CORR.0000000000002308. The author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request. The opinions expressed are those of the writer, and do not reflect the opinion or policy of CORR® or The Association of Bone and Joint Surgeons®.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".