Bibliographic record
Abstract
The use of complementary and alternative medicine (CAM) is currently widespread and appears to be growing. As an increasing proportion of the population turns to CAM therapies, whether singly or in combination with allopathic medicine, the need for quality research in this area is reinforced. Much of this research consists of clinical studies aimed primarily at clinicians, yet challenges arising from poor methodological quality will occur when interpreting study findings and their implications. For clinicians to be effective consumers of the scientific literature, familiarization with the principles of evidence-based medicine (EBM) is essential. The goal of this review is to introduce clinicians to the concept of critical appraisal of clinical studies and foster critical thinking when reading research articles in order to best evaluate and incorporate study findings into their daily practice. Topics discussed in this article include: (1) fundamentals of EBM; (2) types of clinical studies; (3) hierarchy of evidence; (4) Consolidated Standard of Randomized Trials (CONSORT) statement to evaluate the quality of reporting in randomized controlled trials (RCTs); (5) methodologic quality rating scales for RCTs; and (6) issues specific to evaluating studies of Chinese herbal medicine.
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 | 0.434 | 0.748 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.029 | 0.018 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".