Incorporating remote patient monitoring in virtual pulmonary rehabilitation programs
Bibliographic record
Abstract
Most pulmonary rehabilitation (PR) programs have had to adapt due to the COVID-19 pandemic and associated restrictions. Current alternative home-based programs have limitations and require modification. In this paper, we outline a novel method to monitor home-based PR programs, which has the potential to improve PR safety and efficacy. This new method is based on a remote patient monitoring (RPM) system with connected smart devices that enables the Respiratory Therapist (RT) to have real-time access to patient data including heart rate and peripheral oxygen saturation during exercise. The RPM system also monitors daily physical activity, sedentary time, sleep quality, rescue inhaler use, and maintenance inhaler adherence, among other variables, which has the added advantage of predicting patterns consistent with symptoms that may require medical intervention. To increase privacy, data are anonymized at all levels and only the RT has access to patient information. RPM systems have the potential to give practitioners a holistic view of the participants' health status to better evaluate them during the entire PR program and to improve self-management. As this is not a formal research study, we cannot make definitive conclusions about the efficacy of the system, and further research is needed to examine safety and to compare our approach to other ways of conducting PR.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".