Designing a Surveillance System in Canada to Detect Adverse Interactions Between Traditional Chinese Medicine and Western Medicine: Issues and Considerations
Bibliographic record
Abstract
The use of Chinese medicinal materials (CMM) in the context of Traditional Chinese Medicine (TCM) is increasingly prevalent in Canada and worldwide. Self-medication with CMM and concurrent usage with Western medicine are also common. While taking CMM carries recognized risk of adverse effects on their own, their interactions with Western medicine can further generate additional adverse effects but are largely underestimated and undetected due to under-reporting. This is especially true in Canada and other Western countries where CMM is regulated as natural products. Currently worldwide, surveillance of CMM is variable and primarily through spontaneous and voluntary reporting systems. Current approaches in the Western world, including Canada, are by-and-large ineffective in detecting CMM-Western medicine adverse interactions. We propose the development of a Canadian surveillance system for CMM usage that involves both health professionals and patients so as to increase detection of potential adverse reactions and improve safety. As a first step, we will carry out surveys and focus groups with the stakeholder groups to identify desirable features of such a surveillance system as important ground work.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".