Similar outcomes among general medicine patients discharged on weekends
Bibliographic record
Abstract
BACKGROUND: Hospitals reduce staffing levels and services on weekends. This raises the question of whether weekend discharges may be inadequately prepared and thus at higher risk for adverse events postdischarge. OBJECTIVE: To compare death or nonelective readmission rates 30 days after weekend versus weekday discharge. DESIGN: Retrospective cohort. SETTING: All teaching hospitals in Alberta, Canada. PATIENTS: General internal medicine (GIM) discharges (only 1 per patient). MEASUREMENTS: Analyses were adjusted for demographics, comorbidity, and length of stay based on a previously validated index. RESULTS: Of 7991 patients (mean age, 62.1 years; 51.9% male; mean Charlson 2.56; 57.5% LACE ≥10) discharged from 7 teaching hospitals, 1146 (14.3%) were discharged on a weekend. Although they had substantially shorter lengths of stay (5.64 days, 95% confidence interval [CI]: 5.35-5.93 vs 7.86 days, 95% CI: 7.71-8.00, adjusted P value < 0.0001) and were less likely to be discharged with homecare support (10.9% vs 19.3%) or to long-term care facilities (3.1% vs 7.8%), patients discharged on weekends exhibited similar rates of death or readmission at 30 days compared to those discharged on weekdays (10.6% vs 13.2%, adjusted odds ratio [aOR]: 0.94, 95% CI: 0.77-1.16), even among the 4591 patients deemed to be at high risk for postdischarge events based on LACE (length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission) score ≥10 (16.8% vs 16.5% for weekday discharges, aOR: 1.09 [95% CI: 0.85-1.41]). CONCLUSIONS: GIM patients discharged from teaching hospitals on weekends have shorter lengths of stay and exhibit similar postdischarge outcomes as patients discharged on weekdays.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".