It's not you, it's me: time to narrow the gap in weekend care
Bibliographic record
Abstract
Numerous studies1–6 have now described the ‘weekend effect’ and its negative impact on timeliness of inpatient care and mortality. Patients presenting with numerous medical and surgical problems experience better outcomes if they happen to arrive in hospital on a workday versus a weekend day. Researchers have highlighted the problem as reflecting reduced staffing and decreased access to specialised services at the weekend. Remarkably, the weekend effect was recently found to extend even to elective admissions and surgeries, raising major questions about resource planning around elective care that occurs near the end of the week.7 ,8 Perez Concha et al 9 analysed administrative data from Australian hospitals between 2000 and 2007. They compared 7-day mortality after hospital admission in patients admitted during the weekend versus a weekday, stratifying their analysis by diagnostic group. They defined the weekend as occurring between midnight on Friday and midnight on Sunday. Their analysis included ORs for death after weekend versus weekday admission, as well as survival curves and HRs. In all, 16 of 430 diagnostic groups showed evidence of a weekend effect for 7-day patient mortality. No conditions demonstrated an ‘inverse weekend effect’, or decreased mortality over the weekend relative to the work week. In addition to risk ratios, the authors present the absolute number of excess deaths for each condition. All together, these conditions account for 21 excess deaths per 1000 patient admissions. Adjustment for differences in case mix between weekend and weekday groups surprisingly increased the magnitude of the observed weekend …
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".