ArticleBMC medical research methodology2020
WHO Trial Registration Data Set (TRDS) extension for traditional Chinese medicine 2020: recommendations, explanation, and elaboration.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Reporting characteristics and quality of randomized controlled trial protocols in traditional Chinese medicine: a cross-sectional study.Frontiers in pharmacology · 2024Pooled it
- Plain language summaries: A systematic review of theory, guidelines and empirical research.PloS one · 2022Pooled it
- Assessing Organoleptic Properties Simulation of a Placebo of Raw Food Material to a Chinese Medicine Oral Liquid FYTF-919.BioMed research international · 2026Trial
- Elevating traditional Chinese medicine in global health research: The case for Chinese herbal medicine formulas in mainstream therapeutics.Innovation (Cambridge (Mass.)) · 2025Review
- Article
- Quality assessment and its influencing factors of lung cancer clinical research registration: a cross-sectional analysis.Journal of thoracic disease · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundAlthough the WHO Trial Registration Data Set (TRDS) has been published for many years, the quality of clinical trial registrations with traditional Chinese medicine (TCM) is still not satisfactory, especially about the inadequate reporting on TCM interventions. The development of the WHO TRDS for TCM Extension 2020 (WHO TRDS-TCM 2020) aims to address this inadequacy.
methodsA group of clinical experts, methodologists, epidemiologists, and editors has developed this WHO TRDS-TCM 2020 through a comprehensive process, including the baseline survey, draft of the initial items, three-round of Delphi survey, solicitation of comments, revision, and finalization.
resultsThe WHO TRDS-TCM 2020 statement extends the latest version (V.1.3.1) of TRDS published in November 2017. The checklist includes 11 extended items (including subitems), namely Source(s) of Monetary or Material Support (Item 4), Scientific Title (Item 10a and 10b), Countries of Recruitment (Item 11), Health Condition(s) or Problem(s) Studied (Item 12), Intervention(s) (Item 13a, 13b and 13c), Key Inclusion and Exclusion Criteria (Item 14), Primary and Key Secondary Outcomes (Item 19 to 20), and Lay Summary (Item B1). For Item 13 (Interventions), three common TCM interventions--i.e., Chinese herbal medicine formulas, acupuncture and moxibustion-are elaborated.
conclusionsThe group hopes that the WHO TRDS-TCM 2020 can improve the reporting quality and transparency of TCM trial registrations, assist registries in assessing the registration quality of TCM trials, and help readers understand TCM trial design.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.