Home telemonitoring for respiratory conditions: a systematic review.
Bibliographic record
Abstract
OBJECTIVES: To provide a systematic review of home telemonitoring for respiratory conditions and to present evidence on its effects in relation to data quality, patient medical condition, utilization of health services, feasibility and use, and economic viability. STUDY DESIGN: Systematic review of the literature to identify peer-reviewed articles that reported effects of home telemonitoring for patients with respiratory conditions. METHODS: A literature search was conducted on Cochrane and MEDLINE (1966-2007) databases using the following keywords: pulmonary, respiratory, asthma, lung, telemonitoring, telecare, telehealth, telehomecare, and home monitoring. RESULTS: Twenty-three studies were found that presented effects of telemonitoring for various pulmonary conditions. Good levels of data validity and reliability were reported, when assessed. However, little quantitative evidence exists about its effects on patient medical condition and utilization of health services. Positive effects on patient behavior were consistently reported. Only 2 studies performed a detailed cost analysis of this approach. CONCLUSIONS: Home telemonitoring of respiratory conditions results in early identification of deteriorations in patient condition and symptom control. Positive patient attitude and receptiveness of this approach are promising. However, evidence on the magnitude of clinical and structural effects remains preliminary, with variations in study approaches and an absence of robust study designs and formal evaluations. Assessment of providers' attitudes toward telemonitoring and its effect on their workload is necessary.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".