Teaching Evidence-Based Complementary and Alternative Medicine: 4. Appraising the Evidence for Papers on Therapy
Bibliographic record
Abstract
Practicing evidence-based complementary and alternative medicine (CAM) requires practitioners to develop an ability to appraise the quality of published studies addressing questions related to their clinical practice. This paper describes a process by which CAM practitioners can determine the validity of studies evaluating therapeutic interventions. The process requires asking two broad questions: (1). Do the treatment and control group begin with the same prognosis? and (2). Do the treatment and control group remain the same with respect to important prognostic factors? Answering these questions requires determining whether studies used effective randomization, preserved randomization through intention-to-treat analyses, used blinding, and had adequate follow-up of trial participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.197 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".