ArticleBMC medical research methodology2024
Accelerating evidence synthesis for safety assessment through ClinicalTrials.gov platform: a feasibility study.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- [Analysis of the global registration status of clinical trials for artificial intelligence medical device].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundStandard systematic review can be labor-intensive and time-consuming meaning that it can be difficult to provide timely evidence when there is an urgent public health emergency such as a pandemic. The ClinicalTrials.gov provides a promising way to accelerate evidence production.
methodsWe conducted a search on PubMed to gather systematic reviews containing a minimum of 5 studies focused on safety aspects derived from randomized controlled trials (RCTs) of pharmacological interventions, aiming to establish a real-world dataset. The registration information of each trial from eligible reviews was further collected and verified. The meta-analytic data were then re-analyzed by using 1) the full meta-analytic data with all trials and 2) emulated rapid data with trials that had been registered and posted results on ClinicalTrials.gov, under the same synthesis methods. The effect estimates of the full meta-analysis and rapid meta-analysis were then compared.
resultsThe real-world dataset comprises 558 meta-analyses. Among them, 56 (10.0%) meta-analyses included RCTs that were not registered in ClinicalTrials.gov. For the remaining 502 meta-analyses, the median percentage of RCTs registered within each meta-analysis is 70.1% (interquartile range: 33.3% to 88.9%). Under a 20% bias threshold, rapid meta-analyses conducted through ClinicalTrials.gov achieved accurate point estimates ranging from 77.4% (using the MH model) to 83.1% (using the GLMM model); 91.0% to 95.3% of these analyses accurately predicted the direction of effects.
conclusionsUtilizing the ClinicalTrials.gov platform for safety assessment with a minimum of 5 RCTs holds significant potential for accelerating evidence synthesis to support urgent decision-making.
Indexed as
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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.