Preventable Drug-related Morbidity Indicators in the U.S. and U.K.
Bibliographic record
Abstract
OBJECTIVE: To qualitatively describe differences between a series of preventable drug-related morbidity (PDRM) indicators in the United States (U.S.) and the United Kingdom (U.K.), after transfer from the U.S. to the U.K. health care setting. METHODS: A preliminary validation was undertaken of the U.S.-derived indicators within the University of Manchester School of Pharmacy, followed by a 2-round Delphi questionnaire of a sample of general practitioners (n=6) and primary care pharmacists (n=10). The main outcome measures were (1) relevance of the U.S. indicators to U.K. primary care prescribing as determined by preliminary validation and (2) the establishment of consensus among the Delphi participants that an indicator represented PDRM. RESULTS: After preliminary validation, 7 of the U.S. indicators and a part of 2 indicators were considered of insufficient relevance to take any further part in the validation process. A further 18 of the U.S.-derived indicators failed to achieve consensus as PDRMs by the U.K. Delphi panel. At the end of the validation process, 19 indicators remained. CONCLUSIONS: Many of the U.S.-derived indicators lacked relevance in the U.K. due to differences in transatlantic clinical practice. In addition, there may be differences in the philosophical viewpoints of health professionals practising in the U.S. and the U.K. In practice, it is therefore inappropriate to transfer quality indicators of this nature directly from the U.S. to the U.K. However, if some form of validation process is undertaken, indicators derived in one health care setting appear to provide a very useful starting point for those developed in another.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".