Bibliographic record
Abstract
Performance measurement is a cornerstone of efforts to improve health care quality. Mortality rates have been the most commonly used performance metric historically, and they remain so even today. Some mortality rates focus on specific conditions or procedures, such as acute myocardial infarction (AMI) or coronary artery bypass grafting surgery (CABG) patients. At the other end of the spectrum, the overall mortality rate for entire populations (eg, the United States) often is used as a global marker of health, prosperity, and quality of life. Between these extremes, one health care performance metric that is increasingly used is the hospital-wide mor-tality rate, which is conceptually similar to hospital mor-tality rates released by the Health Care Financing Administration (the federal Medicare agency) from 1986 to 1993.1,2 Hospital-wide mortality rates offer the appeal of a single number, which ideally would capture the complexities of overall hospital performance. Health agencies in the United Kingdom, Canada, the Netherlands,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.772 | 0.590 |
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".