Traditional Chinese medicine for adults with overweight and obesity: a protocol for an umbrella review and meta-analysis of randomized controlled trials (Preprint)
Bibliographic record
Abstract
BACKGROUND Many of these systematic reviews have focused on a single modality of TCM for adults with overweight and obesity. OBJECTIVE To compare the efficacy of different traditional Chinese medicine (TCM) therapies for for adults with overweight and obesity and provide a higher level of evidence in the form of umbrella review of systematic reviews and meta-analysis of randomized controlled trials. METHODS Systematic reviews (SRs) and meta analyses (MAs) of TCM for adults with overweight and obesity will be identified and retrieved in databases of Wanfang, China National Knowledge Infrastructure (CNKI), Chinese Biological Medicine (CBM), PubMed, Embase, and Cochrane Database of Systematic Review (CDSR) from their establishment to March 2025. The included SRs/MAs reported body mass index (BMI), body weight, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), low-density lipoprotein cholesterol, total cholesterol, triglycerides, fasting plasma glucose, fasting insulin, homeostatic model assessment of insulin resistance, and blood pressure will be extracted independently by two authors. In cases of disagreement, a consensus will be reached by consulting a third author. GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) analysis using the guideline development tool and AMSTAR-2 (A Measure Tool to Assess Systematic Reviews-2) will be used to evaluate the methodological quality of SRs and MAs, respectively. The meta-analysis will be conducted by R studio (metaumbrella packages) under the random-effects model, as well as assessment of heterogeneity, tests for small-study effects, tests for excess statistical significance, and Jackknife leave-one-out analysis. P value <0.05 will be considered statistically significant. RESULTS No Applicable CONCLUSIONS No Applicable CLINICALTRIAL PROSPERO (CRD42024551788)
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 | 0.134 | 0.183 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.080 | 0.016 |
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".