Researching complementary and alternative treatments – the gatekeepers are not at home
Bibliographic record
Abstract
BACKGROUND: To explore the strengths and weaknesses of conventional biomedical research strategies and methods as applied to complementary and alternative medicine (CAM), and to suggest a new research framework for assessing these treatment modalities. DISCUSSION: There appears to be a gap between published studies showing little or no efficacy of CAM, and reports of substantial clinical benefit from patients and CAM practitioners. This "gap" might be partially due to the current focus on placebo-controlled randomized trials, which are appropriately designed to answer questions about the efficacy and safety of pharmaceutical agents. In an attempt to fit this assessment strategy, complex CAM treatment approaches have been dissected into standardized and often simplified treatment methods, and outcomes have been limited. Unlike conventional medicine, CAM has no regulatory or financial gatekeeper controlling their therapeutic "agents" before they are marketed. Treatments may thus be in widespread use before researchers know of their existence. In addition, the treatments are often provided as an integrated 'whole system' of care, without careful consideration of the safety issue. We propose a five-phase strategy for assessing CAM built on the acknowledgement of the inherent, unique aspects of CAM treatments and their regulatory status in most Western countries. These phases comprise: 1. Context, paradigms, philosophical understanding and utilization 2. Safety status 3. Comparative effectiveness. 4. Component efficacy 5. Biological mechanisms. SUMMARY: Using the proposed strategy will generate evidence relevant to clinical practice, while acknowledging the absence of regulatory and financial gatekeepers for CAM. It will also emphasize the important but subtle differences between CAM and conventional medical practice.
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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 | 0.079 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".