Observational study on the occurrence of surgical glove perforation and associated risk factors in large animal surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of and associated risk factors for glove perforation in large animal surgery. STUDY DESIGN: Prospective observational cohort study. SAMPLE POPULATION: Surgical gloves (n = 917) worn during 103 large animal surgical procedures. METHODS: Gloves worn by personnel involved in sterile preparation and surgical procedures were tested for perforation by 2 previously validated methods, water leak test (WLT) and electroconductivity testing (ECT). The association between surgical and glove-related variables and glove perforation was assessed by using a multivariable mixed-effect logistic regression model. RESULTS: At least 1 glove perforation was detected in 66% of surgical procedures, and 17.9% (164/917) of gloves tested were identified as perforated. All perforations were detected by ECT, whereas only 110/178 (61.8%) were detected by WLT. All perforations detected by WLT were also detected by ECT. The risk of glove perforation increased with duration of wear (>60 minutes odds ratio [OR] 2.3, 95% CI 1.4-3.7; P < .001) and with invasiveness of procedures (OR 7.9, 95% CI 3.2-19.5; P < .001). Primary surgeons were at higher risk for glove perforation than first (OR 1.7, 95% CI 1.1-2.5; P = .008) and second (OR 3.4, 95% CI 2-6.7; P < .001) assistants. Only 25% of glove perforations were detected intraoperatively by the wearer. CONCLUSION: Incidence of glove perforation is similar in large animal, human, and small animal surgery and is influenced by duration of wear, invasiveness of the surgery, and role of the wearer. ECT is more sensitive than WLT for detection of glove perforation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".