Community pharmacists' identification of natural health product/drug interactions in older persons
Bibliographic record
Abstract
Abstract Objective To document the prevalence and significance of potential natural health products (NHPs)/prescribed drug interactions in a sample of older adults; to determine whether community pharmacists detected these drug interactions; and to characterise users and non-users of NHPs. Setting The project involved 15 community pharmacists providing pharmaceutical care to 213 non-institutionalised older adults. Method The study was a subanalysis of a prospective, non-randomised, before-and-after trial of the provision of pharmaceutical care. Pharmacists documented each time medication-specific information or advice was provided to subjects. The numbers and types of NHPs that clients reported taking and the number of potentially significant NHP/prescribed drug interactions were determined. Whether pharmacists identified such drug interactions and made the necessary interventions were also documented. Results Forty-two NHPs were reported 96 times by 49 (23%) clients, most commonly glucosamine (n = 10), garlic (n = 10), prune juice (n = 9), and Ginkgo biloba (n = 6). There was a total of 446 possible NHP/prescribed drug combinations in the 49 clients, of which 53 (12%) were considered to be of potential clinical significance. Of these 53 combinations, three pharmacists identified four (8%) potential interactions in three different patients. Although gender, mean age and number of reported medical conditions did not differ between users and non-users of NHPs, users reported taking fewer prescribed drugs compared with non-users (5.0 ± 3.2 vs 6.0 ± 2.9, respectively, P = 0.043) and more non-prescribed drugs (4.2 ± 2.5 vs 2.1 ± 2.0, respectively, P < 0.0001). Conclusion The reported prevalence of NHP and the potential for NHP/prescribed drug interactions in our sample of older adults were high. Pharmacists providing pharmaceutical care did not commonly identify potentially significant NHP/prescribed drug interactions.
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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 | 0.002 | 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 | 0.001 |
| 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".