Impact of fatigue in surgeons on performance and patient outcome: systematic review
Bibliographic record
Abstract
BACKGROUND: While fatigue is an inevitable aspect of performing surgical procedures, lack of consensus remains on its effect on surgical performance. The aim of this systematic review was to assess the effect of non-muscular fatigue on surgical outcome. METHODS: MEDLINE and Embase were searched up to 17 January 2023. Studies on students, learning, duty-hour restrictions, muscle fatigue, non-surgical or subjective outcome, the weekend effect, or time of admission were excluded. Studies were categorized based on real-life or simulated surgery. The Cochrane risk-of-bias tool was used to assess RCTs and the Newcastle-Ottawa scale was used to assess cohort studies. Due to heterogeneity among studies, data pooling was not feasible and study findings were synthesized narratively. RESULTS: From the 7251 studies identified, 134 studies (including 1 684 073 cases) were selected for analysis (110 real-life studies and 24 simulator studies). Of the simulator studies, 46% (11 studies) reported a deterioration in surgical outcome when fatigue was present, using direct measures of fatigue. In contrast, only 35.5% (39 studies) of real-life studies showed a deterioration, observed in only 12.5% of all outcome measures, specifically involving aggregated surgical outcomes. CONCLUSION: Almost half of simulator studies, along with one-third of real-life studies, consistently report negative effects of fatigue, highlighting a significant concern. The discrepancy between simulator/real-life studies may be explained by heightened motivation and effort investment in real-life studies. Currently, published fatigue and outcome measures, especially in real-life studies, are insufficient to fully define the impact of fatigue on surgical outcomes due to the absence of direct fatigue measures and crude, post-hoc outcome measures.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".