Weekend admissions as an independent predictor of mortality: an analysis of Scottish hospital admissions
Bibliographic record
Abstract
OBJECTIVES: Weekend admissions have been shown to be associated with an increased risk of mortality compared with weekday admissions for many diagnoses. We analysed emergency department admissions within the Scottish National Health Service to investigate whether mortality is increased in case of weekend emergency department admissions. DESIGN: A cohort study. SETTING: Scotland National Health Service (NHS) emergency departments. PARTICIPANTS: 5 271 327 emergency department admissions between 1999 and 2009. We included all patients admitted via emergency departments recorded in the Scottish Morbidity Records (SMR01) in NHS, Scotland for whom complete demographic data were available. PRIMARY OUTCOME MEASURES: Death as recorded by the General Register Office (GRO). RESULTS: There was a significantly increased probability of death associated with a weekend emergency admission compared with admission on a weekday (unadjusted OR 1.27, 95% CI 1.26 to 1.28, p<0.0001; adjusted for year of admission, gender, age, deprivation quintile and number of comorbidities OR 1.42, 95% CI 1.40 to 1.43, p<0.0001). CONCLUSIONS: Despite a general reduction in mortality over the last 11 years, there is still a significant excess mortality associated with weekend emergency admissions. Further research should be undertaken to identify the precise mechanisms underlying this effect so that measures can be put in place to reduce patient mortality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".