Hospital readmission among older adults with congestive heart failure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it