Systematic reviews of TCM trials: how does inclusion of Chinese trials affect outcome?
Bibliographic record
Abstract
AIM: Systematic reviews (SRs) are an important tool for the synthesis of research and are used to guide both research and clinical practice. Previous research suggests that changes to standard SR methodology may be warranted. The objectives of this study were to determine the value of adding Chinese-language databases to conventional systematic review (SR) search strategies, and ii) to determine the importance of methodological validation of TCM RCTs in the conduct of SRs of two health conditions, chronic fatigue syndrome (CFS) and EBV-infectious mononucleosis (mono). METHODS: Ten English-language and two Chinese-language databases were searched from inception to 2008. After initial screening potentially relevant publications were retrieved and assessed based on predetermined inclusion criteria. Method of randomization was verified using author interviews. RESULTS: Mono Search - While English-language database searches did not yield any potentially relevant references, Chinese-language database searches identified 14 studies labelled as RCTs. Author interview determined that 10 were clinical summaries and one a controlled clinical trial. Authors for three publications were unavailable. CFS Search - English-language and Chinese-language database searches identified 8 and 28 potentially relevant references, respectively, for a total of 36, however, none met all inclusion criteria. CONCLUSIONS: Utilization of Chinese-language databases greatly increased the number of potentially relevant references for each search. Unfortunately, due to methodological flaws, this additional information did not generate any usable information. Medical research in China continues to be active, including the conduct of RCTs, however, improvements in trial design and conduct in medical research in China are essential in order for this material to be useful in guiding research and practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
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 | 0.584 | 0.825 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".