A Scoping Review of Heart Failure Transitional Care Quality Indicators and Outcomes for Use in Clinical Care and Research
Bibliographic record
Abstract
AIMS: There are no accepted quality indicators for transitional care following hospitalization for heart failure (HF). Current quality measures focus on 30-day readmissions without accounting for competing risks such as death. In this scoping review of clinical trials, we aimed to develop a set of HF transitional care quality indicators for clinical or research applications following hospitalization for HF. METHODS AND RESULTS: We performed a scoping review using MEDLINE, Embase, CINAHL, HealthSTAR, reference lists and grey literature from January 1990 to November 2022. We included randomized controlled trials (RCTs) of adults hospitalized for HF who received a healthcare service or strategy intervention that aimed to improve patient-reported or clinical outcomes. We independently extracted data and performed a qualitative synthesis of the results. We generated a list of process, structure, patient-reported, and clinical measures that could be used as quality indicators. We highlighted process indicators that were associated with improved clinical outcomes and patient-reported outcomes that had high adherence to COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and United States Food and Drug Administration standards. From 42 RCTs included in the study, we identified a set of process, structure, patient-reported, and clinical indicators that could be used as transitional care measures in clinical or research settings. CONCLUSION: In this scoping review, we developed a list of quality indicators that could guide clinical efforts or serve as research endpoints in transitional care in HF. Clinicians, researchers, institutions, and policymakers can use the indicators to guide management, design research, allocate resources, and fund services that improve clinical outcomes.
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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 | 0.072 | 0.249 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.042 | 0.040 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".