The enigma of the weekend effect
Bibliographic record
Abstract
Increased mortality associated with weekend patient admissions is a global and pervasive phenomenon. Particularly in the UK, this has recently been the subject of intense media, political and scientific scrutiny (1,2). The “weekend effect” has often been highlighted with farfetched conclusions regarding the likely causes. Specifically, it has been implied that the weekend effect is a result of the failure of healthcare management organizations to improve processes of care, including ensuring 24/7 accessibility to life-saving diagnostic and therapeutic procedures (2-4). As such, the body of evidence that has emerged over the last few years on the weekend effect has resulted in the UK government implementing a series of changes to facilitate the adoption of 24/7 hospital care across the National Health Services (1,4), engendering much controversy.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.018 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".