Preventable Drug-related Morbidity in Older Adults-1. Indicator Development (Part 1 of a 2-part series)
Bibliographic record
Abstract
OBJECTIVE: To develop viable clinical indicators of preventable drug-related morbidity (PDRM) in older adults. METHODS: A survey was constructed, listing the clinical outcome and pattern of care related to a number of possible PDRMs in older adults. Using the Delphi technique, a geriatric medicine expert panel of 6 physicians and one clinical pharmacist from a hospital-based health care system was asked to judge whether the outcome in each situation was foreseeable and recognizable, and whether causality was identifiable and controllable. The panel could also suggest additional PDRMs. RESULTS: Fifty-two consensus-approved clinical indicators of PDRM in older adults were developed after 2 rounds of the Delphi technique. There was a high degree of consensus among the expert panel: all 7 members agreed on 35 indicators; 6 of 7 members agreed on 15 indicators; and 5 members agreed on 2 indicators. Only 6 outcomes and patterns of care were rejected as indicators. CONCLUSIONS: This phase of the study showed that consensus on clinical indicators of PDRM can be reached among experts. These indicators could be used by a managed care organization to proactively identify patients at risk for a PDRM and to improve the quality, safety, and appropriateness of medication use. Additionally, the indicators form an important bridge between processes and outcomes of care and could be used in conjunction with HEDIS and other performance indicators.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".