Post-market surveillance of natural health products in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Market trends indicate that natural health products are being used to maintain health as well as prevent and treat many medical conditions. A recent Canadian survey showed that 71% of the Canadian population have used a natural health product. Among these, many reports that they take natural health products on a daily basis. This review emphasizes on Canadian post-market surveillance system that apply to natural health products for human use. The public's perception is that the natural health products are all-natural, safe and effective, but there is still a wide variety of harms linked with these products. The postmarket surveillance system is the monitoring window to observe and control the adverse effects of using natural health products. There are many activities involved in the post-surveillance to ensure the quality of the approved natural health products. Despite the fact that post-market surveillance plays a very important role in eliminating and/or reduce the risk of using natural health products, there are still some challenges and more work to be done to improve the outcome of the post-market surveillance of the natural health products.
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.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it