How well do pharmacists know their patients? A case report highlighting natural health product disclosure
Bibliographic record
Abstract
Natural health products (NHPs), a broad category that includes vitamins, minerals, herbs, homeopathic remedies, traditional medicines, probiotics, amino acids and fatty acids, are used to maintain and promote health, as well as to prevent or treat illness.1 Many consumers report using NHPs because they are perceived to be healthier or safer than conventional drugs.2 A Health Canada survey found that 73% of Canadians have reported using at least 1 NHP, and 20% believe NHPs are without adverse effects.2 As NHPs are available without a prescription, patients often use them to treat their medical conditions, based on advice obtained from the Internet, media sources and/or friends/family.3 Patients also do not consistently disclose NHP use to health care providers, nor are they routinely asked about such use.4-6 This lack of communication may have serious implications; for example, some NHPs, such as kava, have been associated with hepatotoxicity,7 and St. John’s wort may interact with a number of prescription medicines that may lead to failed therapeutic outcomes or increased risk of toxicity.8 Use of these products without health professional input could be inappropriate or lead to delayed recognition of adverse reactions if they occur. Community Pharmacy SONAR (Study Of Natural health product Adverse Reactions) is a multicentre study investigating adverse events (AEs) associated with the use of prescription drugs, NHPs and their concurrent use through the implementation of active surveillance. Consenting patients who reported an AE while also taking an NHP (both alone or concurrently with prescription drugs) were contacted by a research pharmacist (CN) to collect a detailed medical history. Detailed methods are available elsewhere.9 This study was approved by the Human Research Ethics Board at the University of Alberta. Here, we present a detailed case history of a SONAR study participant. The patient’s medication history is presented in a stepwise fashion to illustrate what can be learned when a pharmacist further questions a patient about NHP use—the initial information available is typical of routine pharmacy practice; the subsequent additive information can be learned when additional details are sought by the pharmacist.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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 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".