Methodological Issues Pertaining to the Evaluation of the Effectiveness of Energy-Based Therapies, Avenues for a Methodological Guide
Bibliographic record
Abstract
The current interest in Complementary and Alternative Medicine (CAM) begs the question of their integration into the health care system, which will most likely require rigorous scientific evaluation in randomized controlled trials (RCT) before they are fully accepted and integrated. Although some meta-analyses demonstrate the potential of certain energy-based (EB) CAM therapies others highlight significant methodological weaknesses in the study design. It is not only important to verify the effectiveness of energy-based therapies (EBT), but also to do it with methods that are appropriate to the evaluation of this type of therapy. In fact, there are those who question the applicability of traditional research models to the evaluation of CAM therapies. It is with this in mind that we wish to suggest certain parameters that should be taken into account when planning a research for the evaluation of CAM therapies and meta-analyses.
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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 | 0.740 | 0.857 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".