Hospital readmission among older adults with congestive heart failure
Bibliographic record
Abstract
INTRODUCTION: To examine the factors associated with unplanned readmission among older adults with congestive heart failure (CHF) within 28 days of discharge from an index admission, within a large Australian health service. METHODS: Using a comparative cohort design, a multivariate logistic regression model was used to compare readmitted patients with non-readmitted patients and identify risk factors associated with readmission. RESULTS: Significant risk factors identified were male gender, numerous diagnoses, length of stay 3 days or longer and patients being admitted from acute, subacute or aged-care facilities. CONCLUSIONS: The high risk of patients being readmitted from acute, subacute and aged-care services requires further review as these readmissions may be avoidable. It may also be useful to develop a readmission risk screening tool so that patients at risk of readmission can be identified. What is known about this topic? Older adults with CHF are likely to experience multiple readmissions to hospital. There have been several studies conducted on hospital readmissions; however, generalising the findings is problematic due to the use of variable definitions of what constitutes a readmission. What does this paper add? This paper addresses the absence of Australian research comparing groups of older patients with CHF who are readmitted to hospital with those who are not readmitted. It also adopts one of the more frequently used definitions of readmission to aid in future comparability of research. What are the implications for practice? Further work is necessary to improve discharge planning and effectively manage chronic illnesses such as CHF in patients' homes. It may be useful to develop a readmission risk screening tool for staff of inpatient medical wards so that these at-risk patients can be identified before discharge.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".