Automated E-Counseling for Chronic Heart Failure
Bibliographic record
Abstract
Background: International task force statements advocate telehealth programs to promote health-related quality of life for patients with chronic heart failure (CHF). To that end, we evaluated the efficacy and usability of an automated e-counseling program. Methods: This Canadian multi-site double-blind randomized trial assessed whether usual care plus either internet-based e-counseling (motivational and cognitive-behavioral tools for CHF self-care) or e-based conventional CHF self-care education (e-UC) improved 12-month Kansas City Cardiomyopathy Questionnaire Overall Summary (KCCQ-OS). Secondary outcomes included program engagement (total logon weeks, logons, and logon hours), total CHF self-care behaviors, diet (fruit and vegetable servings), 6-minute walk test, and 4-day step count. The association between program engagement and health-related quality of life was assessed using KCCQ-OS tertiles. Results: We enrolled 231 patients, median age =59.5 years, 22% female, and elevated median KCCQ-OS=83.0 (interquartile range, 68–93). KCCQ-OS increase ≥5 points was not more prevalent for e-counseling, n=29 (29.6%) versus e-UC, n=32 (34.0%), P =0.51. E-Counseling versus e-UC increased total logon weeks ( P =0.02), logon hours ( P =0.001), and logons ( P <0.001). Only e-counseling showed a positive association between 12-month KCCQ-OS tertile and logon weeks ( P =0.04) and logon hours ( P =0.004). E-Counseling increased CHF self-care behavior and diet but not 6-minute walk test or 4-day step count. Conclusions: The primary KCCQ-OS end point was negative for this trial. Only e-counseling showed a positive association between program engagement and 12-month KCCQ-OS tertile, and it improved CHF self-care behavior and diet. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01864369.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".