Risk of Bias Tool in Systematic Reviews/Meta-Analyses of Acupuncture in Chinese Journals
Bibliographic record
Abstract
BACKGROUND: Use of a risk of bias (ROB) tool has been encouraged and advocated to reviewers writing systematic reviews (SRs) and meta-analyses (MAs). Selective outcome reporting and other sources of bias are included in the Cochrane ROB tool. It is important to know how this specific tool for assessing ROB has been applied since its release. Our objectives were to evaluate whether and to what extent the new Cochrane ROB tool has been used in Chinese journal papers of acupuncture. METHODS: We searched CBM, TCM database, CJFD, CSJD, and the Wanfang Database from inception to March 2011. Two reviewers independently selected SRs that primarily focused on acupuncture and moxibustion, from which the data was extracted and analyzed. RESULTS: A total of 836 SRs were identified from the search, of which, 105 were included and four are awaiting assessment. Thirty-six of the 105 SRs were published before release of the Cochrane ROB tool (up to 2009). Most used the Cochrane Handbook 4.2 or Jadad's scale for risk or quality assessment. From 2009 to March 2011 69 SRs were identified. While "risk of bias" was reported for approximately two-thirds of SRs, only two SRs mentioned use of a "risk of bias tool" in their assessment. Only 5.8% (4/69) of reviews reported information on all six domains which are involved in the ROB tool. A risk of bias graph/summary figure was provided in 2.9% (2/69) of reviews. Most SRs gave information about sequence generation, allocation concealment, blindness, and incomplete outcome data, however, few reviews (5.8%; 4/69) described selective reporting or other potential sources of bias. CONCLUSIONS: The Cochrane "risk of bias" tool has not been used in all SRs/MAs of acupuncture published in Chinese Journals after 2008. When the ROB tool was used, reporting of relevant information was often incomplete.
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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 | 0.344 | 0.685 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.058 | 0.044 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".