ArticlemedRxiv : the preprint server for health sciences2024
Publicly available continuously updated topic specific databases of randomised clinical trials: A scoping review.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conducting systematic reviews of clinical trials is arduous and resource consuming. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. We aimed to map publicly available, continuously updated, topic-specific databases of randomised clinical trials (RCTs). We systematically searched PubMed, Embase, the preprint servers medRxiv, ArXiv, and Open Science Framework, and Google. We described seven features (access model, database architecture, data input sources, retrieval methods, data extraction methods, trial presentation, and export options) and narratively summarised the results. We did not register a protocol for this review. We identified 14 continuously updated clinical trial databases, seven related to COVID-19 (first active in 2020) and seven non-COVID databases (first active in 2009). All databases, except one, were publicly funded and accessible without restrictions. They mainly employed methods similar to those from static article-based systematic reviews and retrieved data from journal publications and trial registries. The COVID-19 databases and some non-COVID databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Two databases included trial appraisal information (risk of bias assessments). Six databases reported aggregate group level results, but only one database provided individual participant data on request. We identified few continuously updated trial databases, and existing initiatives mainly employ methods known from static article -based reviews. The main limitation to create truly live evidence synthesis is the access and import of machine-readable and harmonised clinical trial data.
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.