A Systematic Review of Telemonitoring Technologies in Heart Failure
Bibliographic record
Abstract
AIMS: Heart failure (HF) results in characteristic signs and symptoms including oedema and breathing difficulties. Heart failure is particularly suited to telemonitoring, because patients' signs and symptoms can be assessed remotely by healthcare providers, and deterioration can be quickly detected and addressed. In this paper, we review studies conducted in HF telemonitoring, to describe the nature of the modality, the methods, and the results. METHODS AND RESULTS: Articles were obtained through a MedLine search, utilizing the term heart failure in conjunction with the terms telehealth, telecare, telemonitoring, web, Internet, remote monitoring, and self-monitoring. Studies utilizing various modalities, including telephone touch pad, specialized hardware, and websites for participants to enter data were found, with various benefits being reported. Most studies demonstrated improvements in outcome measures, including improved quality of life and decreased hospitalizations. However, not all studies reported the same improvements and in several cases the sample sizes were relatively small. CONCLUSION: Telemonitoring appears to be an acceptable method for monitoring of HF patients. Controlled, randomized studies directly comparing different modalities and evaluating their success and feasibility when used as part of routine clinical care, are now required.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".