Weekend Hospital Admission, Acute Kidney Injury, and Mortality
Bibliographic record
Abstract
Admission to the hospital on weekends is associated with increased mortality for several acute illnesses. We investigated whether patients admitted on a weekend with acute kidney injury (AKI) were more likely to die than those admitted on a weekday. Using the Nationwide Inpatient Sample, a large database of admissions to acute care, nonfederal hospitals in the United States, we identified 963,730 admissions with a diagnosis of AKI between 2003 and 2006. Of these, 214,962 admissions (22%) designated AKI as the primary reason for admission (45,203 on a weekend and 169,759 on a weekday). We used logistic regression models to examine the adjusted odds of in-hospital mortality associated with weekend versus weekday admission. Compared with admission on a weekday, patients admitted with a primary diagnosis of AKI on a weekend had a higher odds of death [adjusted odds ratio (OR) 1.07, 95% confidence interval (CI) 1.02 to 1.12]. The risk for death with admission on a weekend for AKI was more pronounced in smaller hospitals (adjusted OR 1.17, 95% CI 1.03 to 1.33) compared with larger hospitals (adjusted OR 1.07, 95% CI 1.01 to 1.13). Increased mortality was also associated with weekend admission among patients with AKI as a secondary diagnosis across a spectrum of co-existing medical diagnoses. In conclusion, among patients hospitalized with AKI, weekend admission is associated with a higher risk for death compared with admission on a weekday.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".