The Effects of Health Care Providers Night Shift on Cerebral Oxygen Saturation and its Relationship to Cognitive Function
Bibliographic record
Abstract
Background: In-training physicians frequently experience acute periods of sleep deprivation due to the extended durations of on-call shifts. To safeguard the well-being of residents, ensure patient safety, and optimize their performance, it becomes imperative to thoroughly assess and evaluate the cerebral function of in-training physicians subsequent to their shifts. Objective: To elucidate the effects of the nightshifts on regional cerebral oxygen saturation (rScO₂) and cognitive function of 50 in-training pediatricians. Materials and Methods: The rScO₂ was monitored using Near-infrared spectroscopy at specific intervals, pre-on-call (H0), post-call (H16), 24-hours from pre-on-call (H24), and 48-hours from pre-on-call (H48). All physicians continued their duties without a post-call day. The mathematical tests were performed at H0, H16, H24, and H48, while the Montreal cognitive assessment (MoCA) test was conducted at H0 and H24. Results: The mean rScO₂ at post-call at 65.8±5.3% and 24-hours from pre-on-call at 64.6±6.4%, were significantly declined from pre-on-call at 68.2±5.6% (p<0.001), and then returned to baseline at 48-hours from pre-on-call at 68.9±5.5%. The mathematical test scores were not different. The mean MoCA score was significantly decreased at 24-hours from pre-on-call at H0 with 28.4±1.4 to H24 with 27.7±1.8 (p<0.001). Conclusion: The 16-hours nightshifts and continuous working among residents might cause a transient reduction of rScO₂, which could lead to decreased cognitive function scores. Understanding the effects of stress, fatigue, and sleep deprivation on the cognitive performance of medical residents not only benefits each individual resident, but also promotes a culture of continuous improvement and excellence in medical education. Keywords: Cerebral oxygen saturation; In-training physicians; Sleep deprivation; Work-hours; Cognitive function
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".