Reporting Quality of Oral TCM Systematic Reviews Based on the PRISMA Harms Checklist from 2013 to 2020
Bibliographic record
Abstract
Background. Systematic reviews focusing on the effectiveness of different kinds of healthcare interventions have been widely published, but there were few guidelines for reporting safety concerns before 2016. The PRISMA harms checklist, which was published in 2016, can standardize reporting quality. Objectives. To evaluate the safety information reporting quality of oral traditional Chinese medicine (TCM) in systematic reviews before and after the PRISMA harms checklist was published and to explore factors associated with better reporting. Methods. We searched PubMed, the Cochrane Library, and Embase to identify all systematic reviews using oral TCM as interventions published before (from 2013 to 2015) and after (from 2017 to 2020) the PRISMA harms checklist was published. We used the PRISMA harms checklist to assess the quality of reporting of the safety information to included systematic reviews. Results. In total, 200 systematic reviews were sampled from eligible studies published between 2013 and 2020. Reviews from 2016 were excluded. Scores on the PRISMA harms checklist (23 items) ranged from 0 to 12. A systematic reviews published after 2016 had better reporting quality compared with studies published before 2016 with regard to the title (P = 0.03), results of individual studies (P = 0.016), and risk of bias across studies (P = 0.043). In all included systematic reviews of our study, the state conclusion in coherence with review findings was reported adequately with the proportion of adherence at 95%; other items had a reporting proportion ranging from 0% to 57%. The four essential reporting items of the PRISMA harms checklist also had a low reporting quality ranging from 0% to 4%. Conclusions. Oral TCM systematic reviews reported inadequate safety information before and after the PRISMA harms checklist was published. This survey suggested that the PRISMA harms checklist should be recommended more to both original research and systematic review authors.
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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 | 0.500 | 0.664 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".