Potential for drug interactions in seniors with osteoarthritis.
Bibliographic record
Abstract
OBJECTIVE: To document the potential for drug interactions in seniors with osteoarthritis and to consider the usefulness of computerized support for detecting clinically important interactions. DESIGN: Self-administered mailed survey. One question requested a list of all medications (prescribed drugs and self-care products, including herbal and "natural" health products) taken in the last 7 days. Interactions among all medications were assessed using an on-line software package. SETTING: Three urban primary care practices in Nova Scotia. PARTICIPANTS: Questionnaires were sent to 244 patients aged 65 years and older with physician-confirmed osteoarthritis. MAIN OUTCOME MEASURES: Number of potential interactions and level of clinical significance associated with each. RESULTS: Response rate was 78% (n = 191); 174 respondents (92%) supplied information on medications. Respondents took an average of 4.7 products of which 2.8 were prescription medications and 1.9 were self-care products. A total of 214 potential interactions were identified; 30 (14%) of these were clinically significant. Most interactions involved nonprescription products, most frequently acetylsalicylic acid. Recommendations in 29 of these 30 clinically significant interactions were cautionary, advising such measures as closer monitoring of blood tests, observation for toxic effects, or making patients aware of side effects. Only 1 interaction prompted a recommendation for avoidance. Respondents reported use of 7 different herbal and natural health products; these products were associated with 5 clinically insignificant interactions. CONCLUSION: Risk of drug interactions in seniors might be high, but few interactions are clinically significant. Only 1 found in our study carried a recommendation for avoidance. The on-line program reported all significant interactions, but the high proportion of insignificant interactions (6 : 1) also reported could lead physicians to override computer-generated alerts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".