A clinical tool for evaluating complementary and alternative medicine utilization and risk
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) is widely used in Canada and throughout the world, making it inevitable that family physicians will encounter CAM use in their patients. CAM therapies are highly variable and are not subject to regulation or oversight, making some such modalities potentially dangerous. Presently, CAM use is discussed during standard history taking, but the information gathered may be of limited utility due to the wide variety of CAM that exists; such diversity makes it practically impossible for one physician to know the risks associated with each CAM. Additionally, some CAM may not identified as such by the patient (eg chiropractic) and may not be reported during a standard patient interview. There currently exists no standardized method of collecting a patient’s history of CAM use, or for assessing risk based on the information collected. Here, we present a clinical tool that helps to screen for use of CAM and stratify patients into risk categories accordingly. It also makes suggestions for management and follow-up of these patients according to their risk category. Included are several quick reference tables to enable physicians to rapidly stratify patients into an appropriate category. This test may help to screen patients for CAM use that puts their health at risk, thereby increasing detection, and enabling timely intervention by the physician to prevent adverse events due to CAM use.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".