Editorial: Physio-logging in humans: recent advances and limitations in wearable devices for biomedical applications
Bibliographic record
Abstract
The recent advancements in wearables and machine learning have paved the way for unparalleled approaches to monitor physiological parameters, prevent diseases and medical conditions, and to assist, and treat patients that suffer from them. These approaches also show great potential in studying human physiology in extreme conditions. Wearable devices can provide real-time information about human health and well-being in extreme environments, enabling early detection of any changes or abnormalities in normal physiological function. In addition, wearables and recent advances in physio-logging can alleviate the impact of numerous diseases, and medical conditions globally. These approaches will impact our life also by reducing the cost of healthcare and increasing patients' quality of life. Noteworthy strides have already been accomplished, evoking enthusiasm among patients and researchers alike.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.017 | 0.020 |
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".