Recognition of intraoperative surgical glove perforation: a comparison by surgical role and level of training
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to characterize the risk of glove perforation among surgical team members performing a typical set of trauma procedures, as well as to identify the rate at which these people recognize potential perforations. METHODS: Gloves used in orthopedic trauma room procedures were collected from all participating team members over 2 weeks and were subsequently examined for perforations. Perforation rates based on glove position, type, wearer and procedure were assessed. RESULTS: Perforations were found in 5.9% of gloves; 4.3% of the perforations were found in outer gloves and 1.6% in inner gloves. Among the outer gloves, 30.7% of the perforations were recognized by the wearer at the time of perforation; none of the inner glove perforations were recognized, even when they were associated with an accompanying outer glove perforation. Significantly more perforations were identified in the gloves of attending staff than in those of other team members. Attending staff experienced more perforations than other wearers, regardless of whether they were acting as the primary surgeon or as an assistant. Perforations were more common in open reduction internal fixation and amputation procedures. For open reduction internal fixation procedures, longer operative times were associated with more frequent glove perforations. CONCLUSION: The rates of glove perforation are high in orthopedic trauma surgeries, and often these perforations are not recognized by the wearer. Attending staff are at an elevated risk of glove perforation. It is recommended that all members of the surgical team change both pairs of gloves whenever an outer glove perforation is observed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".