Frequency of risk factors that potentially increase harm from medications in older adults receiving primary care.
Bibliographic record
Abstract
BACKGROUND: Many circumstances elevate patients, especially older adults, risk for drug-related morbidity and misadventures. Understanding the frequency of these situations can help with the design of initiatives to address or alter these circumstances with the aim of reducing medication therapy-related concerns and associated expenditures. OBJECTIVE: To describe the frequency of circumstances that may place older adults at higher risk for drug-related morbidity and misadventures in a large sample of elderly patients visiting family medicine clinics. METHODS: Elderly adults at 7 family medicine practices across Ontario self-completed the 10-item Medication Risk Questionnaire (MRQ). RESULTS: Surveys were completed by 907 patients, with a mean age of 72.4 (SD 10.7) years and a mean number of 4.8 medical conditions (SD 2.3; min-max: 0-14). Many subjects were taking multiple medications (mean 6.9 (SD 3.8; min-max: 0-21)) and over 90% of respondents reported at least one indicator that potentially increases their risk of drug-related morbidity. CONCLUSION: Number of medications, number of medical conditions and number of daily medication doses were the most frequently observed risks for medication-related issues in this large sample of elderly patients visiting family medicine clinics.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".