Main Operating Room Versus Field Sterility in Hand Surgery: A Review of the Evidence
Bibliographic record
Abstract
Introduction: Many of the guidelines that are generally accepted as main operating room best practices are not evidence based. They are based on the concept that if some sterility is good, more must be better. They are not derived from evidence-based sterility. Evidence-based sterility is the study of which of our various sterility practices increase or decrease our infection rates, as opposed to guidelines based on how many bacteria are in the operating room. Methods: This article adds the most important evidence we could find that is not included in the first paper on evidence-based sterility in hand surgery published in 2019. In this review, we also balance the evidence with common sense opinion. Results: The 21st century has seen a rapid rise in the number and reports of hand surgery procedures performed with field sterility outside the main operating room. There is now an abundance of good evidence to support that the rate of infection is not higher when many hand operations are performed with field sterility in minor procedure rooms. Conclusion: Moving hand surgery out of the main operating room to minor procedure rooms should be supported by healthcare providers. The higher cost, increased solid waste, and inconvenience of main operating room surgery are not justifiable for many procedures because it does not reduce the risk of postoperative infection.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".