Indicators of prescribing quality in drug utilisation research
Bibliographic record
Abstract
An invitational expert meeting on indicators of prescribing quality was held on 13-15 May 2004, bringing together-from 19 European countries, the US, Canada, and Australia-40 researchers specialized in the development and application of indicators. The meeting was organized by the European Drug Utilization Research Group (EuroDURG), the Belgian National Health Insurance Institute (RIZIV-INAMI), and the World Health Organisation Regional Office for Europe (WHO-Euro). The field of prescribing quality was defined and delineated from the medical error field. A conceptual grid for classifying quality indicators was discussed, combining two axes (a drug/disease/patient axis and a structure/process/outcome axis). In addition, available databases were listed for continuous monitoring of drug utilization in Europe, with a description of the content and the richness of the collected data, as well as the impact on the potential and limitations to develop quality indicators. The importance of the origin of data for validity assessment was stressed, as data on drug utilization may originate from physician sources (prescribing data), from pharmacist or health insurer sources (distribution data), or directly from patient sources (compliance data). The different aspects of validity and their methods of assessment were listed. An overview of the (in)appropriate uses of indicators was given. The state of the art of the development and application of prescribing quality indicators in all represented countries was made, together with a first draft of a database of prescribing quality indicators, already subjected to validation procedures.
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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 | 0.081 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".