Fatigue Increases the Risk of Injury From Sharp Devices in Medical Trainees Results From a Case-Crossover Study
Bibliographic record
Abstract
BACKGROUND: Extreme fatigue in medical trainees likely compromises patient safety, but regulations that limit trainee work hours have been controversial. It is not known whether extreme fatigue compromises trainee safety in the healthcare workplace, but evidence of such a relationship would inform the current debate on trainee work practices. Our objective was to evaluate the relationship between fatigue and workplace injury risk among medical trainees and nontrainee healthcare workers. DESIGN: Case-crossover study. SETTING: Five academic medical centers in the United States and Canada. PARTICIPANTS: Healthcare workers reporting to employee healthcare clinics for evaluation of needlestick injuries and other injuries related to sharp instruments and devices (sharps injuries). Consenting workers completed a structured interview about work patterns, time at risk of injury, and frequency of fatigue. RESULTS: Of 350 interviewed subjects, 109 (31%) were medical trainees. Trainees worked more hours per week (P<.001) and slept less the night before an injury (P<.001) than did other healthcare workers. Fatigue increased injury risk in the study population as a whole (incidence rate ratio [IRR], 1.40 [95% confidence interval {CI}, 1.03-1.90]), but this effect was limited to medical trainees (IRR, 2.94 [95% CI, 1.71-5.07]) and was absent for other healthcare workers (IRR, 0.97 [95% CI, 0.66-1.42]) (P=.001).Conclusions. Long work hours and sleep deprivation among medical trainees result in fatigue, which is associated with a 3-fold increase in the risk of sharps injury. Efforts to reduce trainee work hours may result in reduced risk of sharps-related injuries among this group.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".