The utility of wearable devices in the perioperative period
Bibliographic record
Abstract
PURPOSE OF REVIEW: Improved perioperative patient monitoring is a crucial step toward better predicting postoperative outcomes. Wearable devices capable of measuring various health-related metrics represent a novel tool that can assist healthcare providers. However, the literature surrounding wearables is wide-ranging, preventing clinicians from drawing definitive conclusions regarding their utility. This review intends to consolidate the recent literature on perioperative wearables and summarize the most salient information. RECENT FINDINGS: Wearable devices measuring cardiac output and colonic motility have recently been piloted with mixed results. Novel measurement techniques for established metrics have also been studied, including photoplethysmography devices for heart rate and blood pressure along with resistance thermometers for temperature. Nuanced methods of synthesizing data have been piloted, including machine-learning algorithms for predicting adverse events and trajectory curves for step count progression. Wearable devices are generally well accepted, although adjuvant support systems have improved patient satisfaction. SUMMARY: Perioperative wearables are valuable tools for tracking postoperative health metrics, predicting adverse events, and improving patient satisfaction. Future research on removing barriers such as technological illiteracy, artifact generation, and false-positive alarms would enable better integration of wearables into the hospital setting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".