Exploring the Need and Benefits of Digital Therapeutics (DTx) for the Management of Heart Failure in India
Bibliographic record
Abstract
Indian heart failure (HF) registries consistently indicate high hospital readmissions and increased mortality rates after HF diagnosis. The challenges of Indian cardiologists in HF management include limited longitudinal data, frequent readmissions, low medication adherence, inadequate monitoring and follow-up, insufficient patient education, and lack of standard guidelines on cardiac rehabilitation. This article outlines the adoption of digital therapeutics (DTx) in HF management as a potential solution to address these challenges. DTx services offer improved medication adherence, early symptom identification, remote vital monitoring, timely intervention, patient education on symptoms, self-awareness, and lifestyle. Overall, DTx for HF comprises a dedicated team of cardiologists, health coaches, care managers, and globally certified connected devices to provide comprehensive and proactive monitoring, personalized coaching and support, behavioral engagement to improve adherence, emergency response system, delivery of medications and diagnostic tests at home, and a dedicated application for caregivers. DTx has the potential to enhance HF management in India.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".