Time to talk about work‐hour impact on anesthesiologists
Bibliographic record
Abstract
BACKGROUND: A physician's fatigue raises significant concerns regarding personal and patient safety. Effects of sleep deprivation on clinical performance and the quality of patient care are major considerations of today's health care environment. AIM: To evaluate the impact of partial sleep deprivation after a 17-h overnight call (3 pm-7 am) on the mood status and cognitive skills of anesthesiologists in an academic clinical hospital setting, as compared to these parameters during regular working hours. METHODS: Taking circadian rhythm into account, the following measures were assessed in 21 pediatric anesthesiologists at two time points over the course of the study; (i) between 7 and 8 am on a regular non call day, and (ii) between 7 and 8 am after a 17-h in-house call (3 pm-7 am). Six mood states were assessed using the Profile of Mood States. A Total Mood Disturbance (TMD) score was obtained as the sum of all mood scores minus vigor. The total score provides a global estimate of affective state. Simple cognitive tests were similarly administered to assess cognitive skills. A two-tailed paired t-test was used to compare data between regular and post call days. A P < 0.05 was used. RESULTS: The study cohort included 21 pediatric anesthesiologists at a tertiary care children's hospital. Tension, anger, fatigue, confusion, TMD, irritability, feeling jittery, and sleepiness were significantly affected (P < 0.05). A decrease in vigor, energy, and confidence was observed after a night call shift (P < 0.05). There was also a decrease in being "talkative" after the call shift (P < 0.05). CONCLUSION: Partial sleep deprivation affects the total mood status of anesthesiologists and impacts their cognitive skills. These findings are particularly relevant in a context of increased work expectation, particularly on clinical performance in our modern medical system. Such observations suggest that there may be changes that impact the safety of our patients and the quality of health care that is provided.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".