Traffic in the operating room during joint replacement is a multidisciplinary problem
Bibliographic record
Abstract
BACKGROUND: Door openings disrupt the laminar air flow and increase the bacterial count in the operating room (OR). We aimed to define the incidence of door openings in the OR during primary total joint arthroplasty (TJA) surgeries and determine whether measures were needed and/or possible to reduce OR staff traffic. METHODS: We recorded the number of door openings during 100 primary elective TJA surgeries; the OR personnel were unaware of the observer's intention. Operating time was divided into the preincision period, defined as the time from the opening of surgical trays to skin incision, and the postincision period, defined as time from incision to dressing application. RESULTS: The mean number of door openings during primary TJA was 71.1 (range 35-176) with a mean operative time of 111.9 (range 53-220) minutes, for an average of 0.64 (range 0.36-1.05) door openings/min. Nursing staff were responsible for 52.2% of total door openings, followed by anesthesia staff at 23.9% and orthopedic staff at 12.7%. In the preincision period, we observed an average of 0.84 door openings/ min, with nursing and orthopedic personnel responsible for most of the door openings. The postincision period yielded an average of 0.54 door openings/min, with nursing and anesthesia personnel being responsible for most of the door openings. CONCLUSION: There is a high incidence of door openings during TJA. Because we observed a range in the number of door openings per surgery, we believe it is possible to reduce this number during TJA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".