What drugs are our frail elderly patients taking? Do drugs they take or fail to take put them at increased risk of interactions and inappropriate medication use?
Bibliographic record
Abstract
OBJECTIVE: To determine whether there were discrepancies between what medications frail elderly outpatients took and what physicians thought they took and whether discrepancies put patients at risk of taking inappropriate drugs and of increasing the potential for drug interactions. DESIGN: Case series. SETTING: Day Hospital Program at St Mary's of the Lake Hospital in Kingston, Ont. PARTICIPANTS: One hundred twenty community-living elderly patients attending the Day Hospital Program in 1998. Three patients and two family physicians declined to participate. MAIN OUTCOME MEASURES: Lists of medications being taken by patients compared with lists of medications in physicians' charts. Category according to explicit criteria that each drug fell into and risk of drug interactions as determined by the Clinidata Drug Interaction Program. RESULTS: Of the 120 patients, 115 had at least one discrepancy between their lists of medications and their physicians' lists. Of the 1390 medications on the lists, 521 (37%) were being taken by patients without their doctors' knowledge, 82 (6%) were not being taken by patients when doctors thought they were, and 133 (10%) were on both patients' and their doctors' lists but with dosages or frequency of administration that were different. More potential drug interactions were identified on patients' lists than on physicians' lists. No increase in risk of inappropriate drug use was identified. CONCLUSION: Family physicians are often unaware of all the medications their patients are actually taking. Medications used by patients without physicians' knowledge increase the likelihood of drug interactions. Family physicians should look at and inquire about all medications, including over-the-counter drugs, their patients are actually taking.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".