Advancing perioperative care with digital applications and wearables
Bibliographic record
Abstract
Digital biomarkers are quantifiable measures collected from digital health technologies that may act as indicators of biological processes 1 . The rapid increase in real-time health information collected from wearable devices has allowed digital biomarkers to emerge as a promising tool to support the diagnosis, monitoring, and treatment of various health conditions 1 . While digital biomarkers have broad applications in health care, their utilization in perioperative medicine represents an emerging area of study, practice development, and implementation science 1 . A discussion of the potential applications of digital biomarkers in perioperative care may be informative to surgeons, anesthesiologists, and other clinicians involved in caring for patients undergoing surgery. This is particularly relevant given multiple recent publications on digital biomarkers in perioperative care 2 , 3 , 4 . In this article, we discuss the potential role of digital biomarkers in the pre-operative, in-hospital, and post-operative phases of care to improve patient outcomes (Fig. 1 ).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".