Bibliographic record
Abstract
Objective. We compared the quality of hospital care for patients on weekends compared to weekdays by evaluating mortality, procedural waiting times, staffing levels and potential predictors. Methods. We used primarily administrative databases analysis supplemented by a telephone survey, expert judgment, and professional association audits. All acute care hospitalizations resulting from an emergency department visit in Ontario, Canada were analyzed from 1988 to 1997 (n = 3,789,917). We examined both hospital discharge abstracts and billing records to the universal health insurance program for admission, in-hospital, and discharge information. We also conducted a telephone survey and an audit of professionally mandated claims by nurses. Experienced clinicians assessed the top one hundred causes of death with respect to the presence of seven theorized criteria. Results. Hospital mortality rates were significantly higher with admissions on a weekend rather than a weekday for patients with three prespecified diagnoses: ruptured abdominal aortic aneurysm (42% vs 36%, P < 0.001), acute epiglottitis (1.7% vs 0.3%, P = 0.04), and pulmonary embolism (13% vs 11%, P = 0.009). Of the top one hundred causes of death, 23 diagnoses (P < 0.001) showed a significantly higher mortality with weekend admission whereas no diagnosis showed a significantly lower mortality with weekend admission (P < 0.001). Clinical assessors were unable to identify common and specific characteristics to predict these 23 diagnoses (only diagnoses with a high in-hospital death rate had a P < 0.05). For seven selected urgent procedures, the mean wait from admission to procedural completion was longer on weekends compared to weekdays (3.9 days vs 3.6 days, P < 0.001). There is a relative decrease in both clinical (8–18%) and non-clinical staffing of (5–30%) on weekends compared to weekdays. Conclusion. More consistent weekend staffing may provide feasible, meaningful, and immediately available ways to improve health care.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".