Indicators of Preventable Drug-related Morbidity in Older Adults
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of preventable drug-related morbidity (PDRM) in older adults in a provider-sponsored network and identify risk factors for PDRM. METHODS: The study was based on a retrospective review of an integrated health care database, using 52 newly developed clinical indicators of PDRM. The incidence of PDRM was determined by identifying individuals in the database who matched an outcome and pattern of care associated with an indicator. Risk factors were determined through a forward inclusion logistic regression model. The subjects in this study were 3,365 older adults enrolled in a hospital-based health care system in Florida in 1997. The principal outcome measure was identification of individuals who matched a PDRM indicator and risk factors for PDRM. RESULTS: Ninety-seven enrollees who matched one or more of 52 PDRM indicators were found in 3,365 older adults, for an overall incidence rate of 28.8 per 1000. The top 5 indicators of PDRM were responsible for 46.8% of all PDRMs found. Regression analysis identified 5 risk factors: 4 or more recorded diagnoses, 4 or more prescribers, 6 or more prescription medications, antihypertensive drug use, and male gender. CONCLUSION: This study demonstrated that clinical indicators can be used in a managed care organization to identify seniors who have experienced a PDRM. The risk model should better prepare managed care organizations to proactively identify patients at risk for PDRM and to optimize medication use in older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".