Complementary and Alternative Medicine: How Do We Know If It Works? Time to Find Out!
Bibliographic record
Abstract
The use of complementary and alternative medicine (CAM) in Canada is increasing. This may be due to a variety of factors, including limitations of current therapy and patient perceptions of safety. The increasing use of CAM is exposing large numbers of patients to various forms of CAM Commentary - patients who might be very different from the populations who have traditionally used the type of CAM in question, including children and pregnant women. It is critically important that therapies involving CAM be evaluated for safety, efficacy and cost-effectiveness in order to determine where they might fit in the healthcare system. One potential approach is the creation of a Canadian Institute of Therapeutics, with a broad mandate to evaluate conventional, complementary, alternative and novel therapies. Such an Institute, in partnership with investigators and conventional and CAM practitioners, might provide a focus and impetus for studies to define where CAM and other therapies are best configured in the Canadian healthcare system.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".