Impact of 24‐Hour On‐Call Shifts on Headache in Medical Residents: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: During 24-hour on-call shifts medical residents are exposed to diverse circumstances such as sleep deprivation and stress. OBJECTIVE: Our aim is to assess the effect of 24-hour on-call shifts on medical residents' headache-related disability. METHODS: The Migraine Disability Assessment Scale (MIDAS), the Headache Impact Test (HIT-6), the Pittsburgh Sleep Quality Index (PSQI), and the Hospital Anxiety and Depression Scale (HADS) questionnaires were administered to medical residents who had never performed on-call shifts at baseline and 6 months after beginning 24-hour on-call shifts. Scores were compared. RESULTS: About 66 medical residents completed this study. About 21.2% (n = 14) had history of migraine, 42.4% (n = 28) had a history of tension-type headache (TTH) and 12.1% (n = 8) had a history of both migraine and TTH. Among medical residents with migraine, the median MIDAS score was significantly higher after starting 24-hour on-call shifts than at a baseline (4.0 vs 8.0; Wilcoxon, P = .001), meaning that, on average, disability increased from little or no disability, to moderate disability. No difference in HIT-6 scores was found. The median score of PSQI and HADS was higher at 6 months (PSQI: 7.0 vs 8.0; P = .003), (HADS: 5.0 vs 8.0; P < .001) for the general group. CONCLUSIONS: In medical residents with migraine, migraine-related disability increased after starting 24-hour on-call shifts. We also found a worsening in depression and anxiety symptoms and self-reported sleep quality in medical residents with and without headache history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".