Edmonton Quality Assessment Tool for Drug Utilization Reviews: EQUATDUR-2
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to develop an instrument that will assist in evaluating the methodological quality of drug utilization reviews (DURs) and studies of prescribing appropriateness. DESIGN: An expert committee followed accepted steps for developing and testing new instruments. Consultations on content, face validity, and scoring of items were solicited from external experts. Seven raters tested an initial version; subsequently, a refined instrument was designed. The Edmonton Quality Assessment Tool for Drug Utilization Reviews (EQUATDUR-2) evaluates 3 domains: sample selection (1 item), data collection (1 item), and data analysis (3 items). Sixteen raters tested EQUATDUR-2 on a random sample of DURs. MEASURES: The study measures were reliability-using random effects interclass correlation coefficients for ratings by individual raters (ICC2,1) and the mean of ratings (ICC2,k)-and variability between DUR quality levels and rater groups. RESULTS: There were significant differences in methodological quality (P <0.001) and in mean scores comparing low-, moderate-, and high-quality DURs. Nonmethodologists' ratings exhibited significant variability (P = 0.03) and tended to be higher. Agreement varied for individual items (ICC2,1, 0.22 to 0.44; ICC2,k, 0.81 to 0.91) and for mean summary ratings (ICC2,1, 0.42 [95% CI, 0.28 to 0.61]; ICC2,k, 0.92 [95% CI, 0.86 to 0.96]). The average time to rate each DUR was 10.0 minutes (95% CI, 9.2 to 10.9). CONCLUSIONS: EQUATDUR-2 is a succinct, self-administered instrument with evidence of validity and reliability. We recommend that > or =2 raters independently assess each DUR and resolve disagreements by consensus. EQUATDUR-2 will help clinicians and decision makers to evaluate the quality of DUR studies and provide a framework for enhancing rigor in the design, conduct, and reporting of DURs.
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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 | 0.113 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".