Embedding Passive Monitoring Into Global Health and Longitudinal Patient Care
Bibliographic record
Abstract
Unlabelled: A multitude of digital health tools have been developed to monitor, record, and predict health-related events in healthy subjects and patients. In clinical settings, although promising advances have resulted in near-term benefits, their use in longer-term studies is often limited due to the level of friction and burden imposed on the subject, often requiring active engagement by the patient with digital devices and/or its interfaces. Herein, we outline how smart ring technologies could form the anchor point for passive patient monitoring systems by offering a near-ideal compromise between device form factor and data capturing capacity. By using wireless technologies, such devices could form integral components of a hub-and-spoke health monitoring system, feeding data to cloud-based patient electronic health records and allowing push-pull actions through bidirectional communication. Such capabilities could have immediate utility in the longitudinal monitoring of patients diagnosed with slow progressing disease such as cardiovascular and neurodegenerative conditions. Moreover, if integrated through provisioned federated wireless networks, the technology could become components of global health care. To be successful, such a grand challenge would naturally require multiple technological, financial, and data privacy obstacles to be overcome. In support of this vision, we outline practical considerations for the development of such systems for specific applications and potential next steps for implementation.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".