A Prospective Pilot Study of the Biometrics of Critical Care Practitioners during Live Patient Care using a Wearable "Smart Shirt"
Bibliographic record
Abstract
Objective: To measure the biometrics (heart rate, heart rate variability) of critical care physicians during live clinical patient scenarios. Design: Participants wore the Hexoskin biometric smart shirt (Hexoskin, Carrè Technologies, Montreal, Quebec) during live clinical activities. Setting: 24-bed tertiary care children’s hospital pediatric intensive care unit. Subjects: Pediatric critical care attendings and fellows. Interventions: Heart rate (HR), respiratory rate (RR), and heart rate variability (HRV) were recorded during clinical shifts. Activities included subject baseline (SB), patient rounds (PR), tracheal intubation (TI), and central line insertion (CL). Measurements and main results: Mean HR for the activities SB, PR, TI, and CL were 81 ± 3.65, 85 ± 4.75, 99 ± 10.83, and 108 ± 8.97 beats per min, respectively. Mean standard deviation dispersion perpendicular and along the axis of identity (SD1/SD2) were 0.244 ± 0.038, 0.220 ± 0.022, 0.180 ± 0.050, and 0.167 ± 0.015, respectively. P values for mean HR, max HR, and HRV were significant when comparing SB with TI (0.010, 0.027, and 0.001) and CL (0.007, 0.001, and 0.012) but not when comparing with PR (0.026, 0.125, and 0.321). Comparison of SD1/SD2 for TI versus CL showed no statistical significance, P=0.578. Poincaré plots confirmed the similar patterns of physiologic activation. Subject baseline and PR plots were fan-shaped, suggesting primary parasympathetic input. TI and CL were torpedo-shaped, suggesting sympathetic activation. Conclusion: Study of the biometrics of physicians as they deliver real-time critical patient care is feasible using wearable technology. Critical care activities requiring not only thought, focus, and planning but also the physical execution of technical skills, such as IT or CL insertion, resulted in higher levels of sympathetic activation. Further study of physicians from various specialties and different levels of experience, the use of stress mitigation techniques, and correlation with procedural success or failure is warranted
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".