Recent research shows maturity in addressing safety issues associated with CAM therapies
Bibliographic record
Abstract
Recent research shows maturity in addressing safety issues associated with CAM therapies ଝComplementary and alternative medicine (CAM) or integrative medicine (IM) is defined in various ways by many different groups.The most "official" definitions describe CAM in terms of its relationship with the dominant health care system (e.g., http://nccam.nih.gov/health/whatiscam) and integrative medicine as the evidence-informed blending of CAM and conventional medicine (e.g., http://www.imconsortium.org/about/home.html),but patients often describe CAM as being natural and safe options for managing a wide variety of health conditions [1-3].Yet as we all know, being natural does not necessarily mean something is safe -some very potent toxins are naturally found in mushrooms, snake venom and berries.Similarly, if a natural product or CAM therapy is expected to have some effect in the human body, it is reasonable to expect that an effect may also cause unwanted consequences for some cases depending on the genetic make-up of the patient, the patient's underlying condition, the dose of the product or therapy, other products and therapies the patient may be using concurrently and/or the quality of product or skill of the practitioner.Patients and practitioners both need accurate and relevant data regarding any risks, just as much as they need data regarding efficacy in order to make informed decisions about CAM products and therapies.This Special Issue of the Journal focuses on safety issues related to CAM products and therapies.While many resources are focused on exploring what works (or does not work), there are equally important questions regarding safety, which are often less prominent in research.There are a number of reasons for this.One is that doing research on the risks associated with a treatment is quite challenging.Although most clinical trials do collect data on adverse events experienced by patients in the trial, the sample sizes in effectiveness or efficacy trials are almost always too small to be able to identify the majority of adverse events.Similarly, clinical trials generally include only ଝ This editorial belongs to the Special Issue: Ensuring and Improving Patients' Safety in Integrative Health Care.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".