Validation of Four Clinical Indicators of Preventable Drug-
Bibliographic record
Abstract
Drug-related morbidity (DRM) results when drug thera-py does not produce the intended therapeutic outcome, either due to treatment failure or the production of a new medical problem.1 Use of drug therapy may be expected to result in some morbidity; in fact, at least half of the DRM that occurs may be preventable (PDRM).2-5 As described by Hepler and Strand,1 DRM may be classified as pre-ventable only if it was preceded by a recognizable drug-re-lated problem for which the causes and adverse outcome or treatment failure must have been foreseeable, identifi-able, and controllable. The costs and consequences resulting from PDRM can be significant. PDRM is reported to account for 3–9 % of hospital admissions, and>50 % of drug-related hospital ad-missions may be considered preventable.6 A recent Cana-dian study estimated that the annual cost of PDRM in old-er adults is $10.9 billion (CND).7 Clinical indicators are tools that have been widely used to assess quality issues related to the use of medicines. Several authors have reported on the development and use of indicators of PDRM in different jurisdictions. MacKin-non and Hepler8 described the development of clinical in-dicators of PDRM in the US that were adapted for use in the UK9 and further evaluated in another US managed care organization database.10 Recently, in Nova Scotia, Canada, further development and validation of the original 52 US PDRM indicators was undertaken.11 These indicators were operationalized retro-spectively, using administrative claims data to determine
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".