Prescribing of potentially inappropriate medications to elderly people
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence and predictors of medications deemed potentially inappropriate for the elderly among family physicians' patients aged 65 and older (seniors) taking multiple prescribed medications. METHODS: Forty-eight randomly selected family practices in 16 towns and cities in Southern Ontario, Canada and 889 of their senior patients were recruited into a randomized trial. We conducted a cross-sectional analysis of prescription insurance data from the provincial universal prescription insurance database over 12 months, from the 777 seniors who completed the trial and agreed to have their data released. The prevalence and patient and physician predictors of use of a potentially inappropriate medication (PIM), as defined by published widely accepted criteria, were examined. RESULTS: The median number of prescriptions filled was 24. Nearly one-fifth (16.3%) of the seniors received at least one prescription for a PIM, with short-acting benzodiazepine prescriptions for longer than 30 days (6.4%) and oxybutynin (3.7%) being the types prescribed most frequently. In univariate and multiple variable analyses, women were found to be statistically significantly more likely to be prescribed a PIM (adjusted OR = 1.6; 95% confidence interval = 1.0-2.4). Age, education, self-rated health, number of health conditions, and number of prescriptions were not associated with PIM use. Physician gender, family medicine certification status, and time since graduation were not significantly associated with PIM prescribing. CONCLUSIONS: Prescribing of PIMs, especially of short-acting benzodiazepines was common in seniors taking multiple medications. Interventions to reduce use of PIM, especially long-term benzodiazepines, are important in primary care.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".