Comparative Effectiveness of Transitional Care Services in Patients Discharged from the Hospital with Heart Failure: A Systematic Review and Network Meta-Analysis
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: To compare the effectiveness of transitional care services in decreasing all-cause death and all-cause readmissions following hospitalization for heart failure (HF). METHODS AND RESULTS: We searched PubMed, Embase, CINAHL, and Cochrane Clinical Trials Register for randomized controlled trials (RCTs) published in 2000-2015 that tested the efficacy of transitional care services in patients hospitalized for HF, provided ≥1 month of follow-up, and reported all-cause mortality or all-cause readmissions. Our network meta-analysis included 53 RCTs (12 356 patients). Among services that significantly decreased all-cause mortality compared with usual care, nurse home visits were most effective [ranking P-score 0.6794; relative risk (RR) 0.78, 95% confidence intervals (CI) 0.62-0.98], followed by disease management clinics (DMCs) (ranking P-score 0.6368; RR 0.80, 95% CI 0.67-0.97). Among services that significantly decreased all-cause readmission, nurse home visits were most effective [ranking P-score 0.8365; incident rate ratio (IRR) 0.65, 95% CI 0.49-0.86], followed by nurse case management (NCM) (ranking P-score 0.6168; IRR 0.77, 95% CI 0.63-0.95), and DMCs (ranking P-score 0.5691; IRR 0.80, 95% CI 0.66-0.97). There was no significant difference in the comparative effectiveness of services that improved each outcome. Nurse home visits had the greatest pooled cost-savings (3810 USD, 95% CI 3682-3937), followed by NCM (3435 USD, 95% CI 3224-3645), and DMCs (245 USD, 95% CI -70 to 559). Telephone, telemonitoring, pharmacist, and education interventions did not significantly improve clinical outcomes. CONCLUSION: Nurse home visits and DMCs decrease all-cause mortality after hospitalization for HF. Along with NCM, they also reduce all-cause readmissions, with no significant difference in comparative effectiveness. These services reduce healthcare system costs to varying degrees.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| 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) | 0.000 | 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 it