Factors Associated With 7-Day Rehospitalization After Heart Failure Admission
Bibliographic record
Abstract
BACKGROUND: Rehospitalizations within 7 days after discharge may reflect the quality of hospital care. OBJECTIVE: We examined factors associated with 7-day readmissions after discharge for heart failure (HF). METHODS: Using a matched pair case-control design, we examined health records for sociodemographic, clinical, and health system factors for patients with a primary diagnosis of HF (ICD-10 I50) discharged alive from all acute care hospitals in Calgary, Alberta, from 2004 to 2012. Logistic regression was used to identify variables associated with 7-day all-cause readmission. RESULTS: We included 382 patients, or 191 in matched pairs, with 41% of readmissions due to HF. Frailty (adjusted odds ratio [aOR], 2.30; 95% confidence interval [CI], 1.41-3.76) and attending physician as specialist (aOR, 2.10; 95% CI, 1.32-3.42) were associated with increased likelihood of readmission. Reduced likelihood of readmission was associated with documented instructions for follow-up with a family physician within 1 week of discharge (aOR, 0.56; 95% CI, 0.36-0.88). All 3 factors were easily abstracted from all patient records, including frailty, which was defined as all 3 of age older than 75 years, 3 or more comorbid conditions, and requiring assistance with activities of daily living. CONCLUSION: Very early readmission to hospital after HF admission is associated with 3 factors that may be easily identified in patient records.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".