P04.51. Study of natural health product adverse reactions (SONAR): active surveillance in community pharmacies
Bibliographic record
Abstract
Participating pharmacists and pharmacy technicians screened consecutive individuals picking up prescription medications about their (1) NHP use, (2) prescription medication use, (3) concurrent NHP/prescription medication use in the previous one month, and (4) the occurrence of potential AEs. If a potential AE was identified and the patient provided written consent, a research pharmacist conducted a guided telephone interview to gather additional detailed information on the AE and medical history of the patient. Over a total of 105 pharmacy weeks, 1119 patients were screened. Of these patients, 409 reported taking prescription drugs only (36%; 95% CI: 33.7-39.4), 41 reported taking NHPs only (3.7%; 95% CI: 2.6-4.8) and 656 reported taking NHPs and prescription medication concurrently (58.6%; 95% CI: 55.7 to 61.5). A total of 58 patients reported a possible AE, which represents 0.98% (95% CI: 0.03 to 1.93) of those taking prescription medications only, 9.8% of those taking NHPs only (95% CI: 0.7% to 18.9) and 7.5% of those taking NHPs and prescription medications concurrently (95% CI: 5.48 to 9.52). Compared to passive surveillance, this study found active surveillance to markedly improve NHP adverse event reporting rates. Active surveillance offers improved quantity and quality of adverse event data, allowing for meaningful adjudication to assess potential harms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".