Building a Multicenter Telehealth Network to Advance Chronic Disease Management
Bibliographic record
Abstract
The use of telehealth solutions has proved to improve clinical management of chronic diseases, expand access to healthcare services and clinicians, and reduce healthcare-related costs. The project aims at improving Heart Failure (HF) management through the utilization of a Telemedicine and Personal Health Records systems that will assist HF specialist in Colombo, Sri Lanka to monitor and consult with remote HF patients. A telehealth network will be built at an international site that connects five remote telehealth clinics to a central clinic at a major University Hospital in Sri Lanka where HF specialists are located. In this study, 200 HF patients will be recruited for nine months, 100 patients will be randomly selected for the treatment group and the other 100 will be selected for the control group. Pre, mid, and post study surveys will be conducted to assess the efficacy and satisfaction levels of patients with both care models. Moreover, clinical outcomes will be collected to evaluate the impact of the intervention on the treatment patients compared to control patients. The research aims at enhancing Heart Failure management through eliminating current health challenges and healthcare-related financial burdens.
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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 | 0.012 | 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".