Effects of a multidisciplinary management program on symptom burden and medication adherence in heart failure patients with comorbidities: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Comorbidities in heart failure (HF) are a complex clinical challenge. There is little data on the benefits of multidisciplinary postdischarge management programs in such patients. This study aimed to examine the effects of a multidisciplinary management program (MMP) on symptom burden and medication adherence in HF patients with comorbidities. METHODS: In this clinical trial study, 94 HF patients with comorbidities were assigned to intervention (n = 47) and control (n = 47) groups by the stratified-random method. The intervention group underwent MMP supervised by a nurse for two months after discharge, including multi-professional visits, telephone follow-ups, and an educational booklet. Medication adherence and symptom burden were assessed using Morisky Medication Adherence Scale (MMAS) and Edmonton Symptom Assessment Scale (ESAS), respectively, on three occasions: Before discharge, six weeks, and eight weeks after discharge. RESULTS: Both groups almost matched at the baseline, and the most frequent comorbidities included myocardial infarction (MI), hypertension, peptic ulcer, and depression, respectively. The interactive effect of time in groups showed that mean changes in total scores of symptom burden and medication adherence were significantly different (P < 0.001) at other time points. A significant increase in medication adherence (P < 0.001) and a significant reduction in the burden of all symptoms were observed in the intervention group compared to the control group from Time 1 to Time 3. CONCLUSIONS: The MMP (targeting comorbidity) is a promising strategy for managing symptoms and medication adherence in HF patients with comorbidities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".