China's overuse of inpatient treatment and routine preoperative testing
Bibliographic record
Abstract
Moving to day case management of most ocular surgery in China is possible China’s medical system is undergoing substantial reforms to improve access, efficiency, and quality of care.1 These changes have prompted recent suggestions in The BMJ on how efficiency could be further improved. Recommended reforms include reducing unnecessary use of intravenous fluid, currently estimated at eight bottles per capita annually,2 and restricting self medication with antibiotics for conditions such as flu, reported by a quarter of respondents in a recent survey by the China Food and Drug Administration.3 Another opportunity for improved efficiency is to stop admitting patients to hospital for non-invasive surgical care, which is common practice in China. Taking ophthalmology as an example, a tertiary public eye hospital in China typically has between 100 and 500 beds. By contrast, Moorfields Eye Hospital in London, one of the …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".