Validation of Four Clinical Indicators of Preventable Drug-Related Morbidity
Bibliographic record
Abstract
BACKGROUND: Clinical indicators are tools that assess quality issues related to the use of medicines. At this time, validated clinical indicators for preventable drug-related morbidity (PDRM) are lacking. OBJECTIVE: To assess the validity and reliability of using population administrative claims data to identify the extent of PDRM in older adults in Canada. METHODS: Four indicators of PDRM related to cerebrovascular and cardiovascular care were chosen for validation. A random sample of cases that represented the indicators and fit the criteria (hits) for PDRM from the retrospective operationalization of the study database and those that did not fit the criteria (near hits) were selected for chart review. One-page abstracts of the cases were prepared for review by a panel of 5 clinical pharmacists. Validity was assessed by calculating sensitivity, specificity, and positive and negative predictive value. Reliability was assessed using reviewers' agreement scores (kappa statistics). RESULTS: Overall, 119 case abstracts were reviewed by each panelist. The sensitivity ranged from 33% to 100% and the specificity from 51% to 71%. Predictive values ranged from 5.3% to 43% (positive) and 90% to 100% (negative). The overall kappa statistic was fair (0.21). CONCLUSIONS: The validity of the 4 assessed PDRM indicators varied. The reliability was fair; however, these indicators may be useful to screen older adults for PDRM.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".