138 THE EFFECTS OF NIGHT CALL ON PEDIATRIC RESIDENTS' COGNITION AND PERFORMANCE
Bibliographic record
Abstract
Introduction The long hours associated with residency training are often thought to contribute to medical errors. Conventionally, long hours were considered vital to medical education, but resident fatigue may result in unfavourable learning experiences. Studies based on fatigued American residents9 cognition and performance has given equivocal results. Disappointingly, few studies in Canada have thoroughly investigated this controversy. Methods The cognitive and skills performances of 11 pediatric residents working night call duty at British Columbia Children9s Hospital were measured using a project specific, computer based test module. Performance evaluations were performed under two situations call (post 24 hours in hospital) and non-call (post regular nights sleep). Call duty testing was performed at pre-call, mid-call and post-call. Non-call testing was completed during consecutive non-call shifts. To limit learning, the residents were randomly assigned a condition order. The test battery consisted of previously validated measures sensitive to sleep deprivation: simple, choice and incompatible reaction time; digit vigilance, and varied mapping memory recall. A performance task designed to evaluate medical calculations was also added. Results More than 42% of residents report working at least 80 hours per week and 64% claim their call duty exceeds 26 hours. Scores of choice and incompatible reaction time were significantly lower in the call condition. Vigilance reaction time was greater in session three compared to session one; however, this interaction was consistent for both conditions. Significantly more time was required to complete the four category varied mapping as compared to the two category condition. Residents performed significantly faster and with fewer errors in consecutive administrations of the medical calculations exam for both call and non-call conditions. Conclusion Cognitive performance was impaired during the call condition for certain tasks and not for others; however, specific reductions in the sensitive measures could not be isolated to the mid-call or post-call sessions. Our results were unable to support the hypothesis that residents are impaired post-call to the point where significant cognitive and performance deficits materialize. On the other hand, residents may be moderately impaired, but their development of fatigue countermeasures encourages a safe level of performance.
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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 | 0.003 |
| 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) | 0.003 | 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".