Readmitted Patients With Heart Failure Sick, Tired, and Symptomatic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Heart failure (HF) readmissions will continue to grow unless we have a better understanding of why patients with HF are readmitted. Our purpose was to gain an understanding, from the patients' perspective, of how patients with HF viewed their discharge instructions and how they felt when they got home and were then readmitted in less than 30 days. METHODS AND RESULTS: We used a qualitative descriptive approach using semistructured interviews with 22 patients with HF. Most participants had multimorbidities, were classified as New York Heart Association class III (n = 13) with reduced ejection fraction (n = 20), and were on home inotrope therapy (n = 13). The overarching theme that emerged was that these participants were sick, tired, and symptomatic. Additional categories within this theme highlight discharge instructions as being clear and easily understood; rich descriptions of physical, emotional, and other symptoms leading up to readmission; and reports of daily activities including what "good" and "not good" days looked like. Moreover, when participants experienced an exacerbation of their HF symptoms, they were sick enough to be readmitted to the hospital. CONCLUSION: Our findings confirm ongoing challenges with a complex group of sick patients with HF, with the majority on home inotropes with reduced ejection fraction, who developed an unavoidable progression of their illness and subsequent hospital readmission.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".