The PATRIARCH Study. Using outcome measures for league tables: Can a North American prediction of admission score be used in a United Kingdom children's emergency department?
Bibliographic record
Abstract
OBJECTIVES: The use of league tables has become predominant in the healthcare culture of the United Kingdom. These tables are often based on measures that are viewed with scepticism by clinicians. This study was designed to test the validity of a North American risk of admission score, the PRISA, for use in a United Kingdom population of accident and emergency (A&E) attendees. METHODS: All attendees to a children's A&E department were scored using the PRISA for a single calendar month (November 2000) RESULTS: 701 children were studied in total. The results show that the PRISA applied to this population gives an area under the receiver operator curve of 0.76. Of the 701 patients studied, 206 (29.4%) were admitted. The PRISA predicted a total of 206.10 admissions. Of the 50 patients discharged with the highest PRISA scores (that is, with the highest likelihood of admission), none were admitted in the 48 hours after their original attendance. CONCLUSIONS: These results show that the PRISA is suitable as a measure of paediatric A&E department performance in the United Kingdom and it is highly promising as a future measure of quality.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".