Decreased heart rate variability in surgeons during night shifts
Bibliographic record
Abstract
BACKGROUND: Heart rate variability (HRV) has been used as a measure of stress and mental strain in surgeons. Low HRV has been associated with death and increased risk of cardiac events in the general population. The aim of this study was to clarify the effect of a 17-hour night shift on surgeons' HRV. METHODS: Surgeons were monitored prospectively with an ambulatory electrocardiography device for 48 consecutive hours, beginning on a precall day and continuing through an on-call (17-h shift) day. We measured HRV by frequency domain parameters. RESULTS: We included 29 surgeons in our analysis. The median pulse rate was decreased precall (median 64, interquartile range [IQR] 56-70 beats per minute [bpm]) compared with on call (median 81, IQR 70-91 bpm, p < 0.001). Increased high-frequency (HF) activity was found precall (median 199, IQR 75-365 ms2) compared with on call (median 99, IQR 48-177 ms2, p < 0.001). The low-frequency:high-frequency (LF:HF) ratio was lower precall (median 2.7, IQR 1.9-3.9) than on call (median 4.9, IQR 3.7-6.5, p < 0.001). We found no correlation between the LF:HF ratio and performance in laparoscopic simulation. CONCLUSION: Surgeons working night shifts had a significant decrease in HRV and a significant increase in pulse rate, representing sympathetic dominance in the autonomic nervous system. TRIAL REGISTRATION: NCT01623674 (www.clinicaltrials.gov).
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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 | 0.002 |
| 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".