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 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".