Pharmacy study of natural health product adverse reactions (SONAR): a cross-sectional study using active surveillance in community pharmacies to detect adverse events associated with natural health products and assess causality
Bibliographic record
Abstract
OBJECTIVES: To investigate the rates and causality of adverse event(s) (AE) associated with natural health product (NHP) use, prescription drug use and concurrent NHP-drug use through active surveillance in community pharmacies. DESIGN: Cross-sectional study of screened patients. SETTING: 10 community pharmacies across Alberta and British Columbia, Canada from 14 January to 30 July 2011. PARTICIPANTS: The participating pharmacy staff screened consecutive patients, or agents of patients, who were dropping or picking up prescription medications. PRIMARY OUTCOME MEASURES: Patients were screened to determine the proportions of them using prescription drugs and/or NHPs, as well as their respective AE rates. All AEs reported by the screened patients who took a NHP, consented to, and were available for, a detailed telephone interview (14%) were adjudicated fully to assess for causality. RESULTS: Over a total of 105 pharmacy weeks and 1118 patients screened, 410 patients reported taking prescription drugs only (36.7%; 95% CI 33.9% to 39.5%), 37 reported taking NHPs only (3.3%; 95% CI 2.4% to 4.5%) and 657 reported taking prescription drugs and NHPs concurrently (58.8%; 95% CI 55.9% to 61.6%). In total, 54 patients reported an AE, representing 1.2% (95% CI 0.51% to 2.9%), 2.7% (95% CI 0.4% to 16.9%) and 7.3% (95% CI 5.6% to 9.6%) of each population, respectively. Compared with patients who reported using prescription drugs, the patients who reported using prescription drugs and NHPs concurrently were 6.4 times more likely to experience an AE (OR; 95% CI 2.52 to 16.17; p<0.001). Combined with data from Ontario, Canada, a national proportion was calculated, which found that 45.4% (95% CI 43.8% to 47.0%) of Canadians who visit community pharmacies take NHPs and prescription drugs concurrently, and of those, 7.4% (95% CI 6.3% to 8.8%) report an AE. CONCLUSIONS: A substantial proportion of community pharmacy patients use prescription drugs and NHPs concurrently; these patients are at a greater risk of experiencing an AE. Active surveillance provides a means of detecting such AEs and collecting high-quality data on which causality assessment can be based.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".