Postdischarge Outcomes in Heart Failure Are Better for Teaching Hospitals and Weekday Discharges
Bibliographic record
Abstract
BACKGROUND: It is unclear whether teaching status or day of discharge influences outcomes after a heart failure hospitalization. METHODS AND RESULTS: We evaluated adults discharged after a heart failure hospitalization between 1999 and 2009 in Alberta, Canada. The primary outcome was death or nonelective readmission 30 days postdischarge. Of 12 216 patients discharged from teaching hospitals and 12 157 patients from nonteaching hospitals, 20 524 (84%) discharges occurred on weekdays. Although they had greater comorbidity and used more healthcare resources before their heart failure hospitalization, patients discharged from teaching hospitals exhibited shorter lengths of stay (adjusted ratio, 0.83; 95% confidence interval [CI], 0.80-0.86) and significantly lower rates of death or readmission in the 30 days after discharge than those discharged from nonteaching hospitals (17.4% versus 22.1%; adjusted hazard ratio [aHR], 0.83; 95% CI, 0.77-0.89). Patients discharged on weekdays were older and had greater comorbidity, yet exhibited significantly lower rates of death or readmission at 30 days than those discharged on weekends (19.5% versus 21.1%; aHR, 0.87; 95% CI, 0.80-0.94). Compared with weekend discharge from a nonteaching hospital, 30-day death/readmission rates were lower for weekday discharge from a nonteaching hospital (aHR, 0.85; 95% CI, 0.77-0.94), weekend discharge from a teaching hospital (aHR, 0.80; 95% CI, 0.69-0.92), and weekday discharge from a teaching hospital (aHR, 0.71, 95% CI, 0.63-0.79). CONCLUSIONS: Patients discharged from teaching hospitals or on weekdays exhibited better outcomes despite having higher risk profiles. Future studies should focus on distinguishing which discharge processes differ between teaching and nonteaching hospitals and between weekdays and weekends to define those that optimize patient 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".