Evaluating the Quality of Randomized Controlled Trials that Examine the Efficacy of Natural Health Products: A Systematic Review of Critical Appraisal Instruments
Bibliographic record
Abstract
The purpose of this project was to conduct a systematic review to identify instruments designed to evaluate the quality of randomized controlled trials (RCTs) of natural health products (NHPs). Instruments were examined for inclusion of items assessing methods, identity and content of the NHP, generalizability of results and instructions for use. Online databases, websites, textbooks and reference lists were searched to identify instruments. Relevance assessment and data extraction of articles were completed by two investigators and disagreements were settled by the third investigator. Data were analyzed using descriptive statistics. Of the 4442 citations identified, 29 were potentially relevant with 16 meeting the criteria for inclusion. None of the instruments stated they were validated; content in the four areas of interest varied considerably. The most common items included randomization sequence generation (100%), blinding (100%), allocation concealment (75%) and participant flow (75%). Only nine of the NHP instruments included at least one item to appraise the specific content of the NHP. The CONSORT Statement for Herbal Interventions most closely addressed the four areas of interest; however, this instrument was specific for herbs. There is a need for the development of a validated instrument for assessment of the quality of RCTs that would be useful for herbs as well as other NHPs.
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 | 0.411 | 0.712 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.026 | 0.021 |
| Bibliometrics | 0.038 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".