ArticleBMJ open2026
Reproducible study identification and selection in systematic reviews: barriers and paths forward.
Article in BMJ open, 2026. 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
7 authors.
Funding
Abstract
Systematic reviews are foundational to evidence-based practice, but the reproducibility and auditability of study identification and selection remain persistent challenges. Existing reporting standards, such as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 and PRISMA-Search extension, have improved transparency, yet important gaps remain in documenting search strategies, retrieved records, screening decisions and supplementary identification methods. In this article, we discuss key barriers to reproducibility, including incomplete or inconsistent search documentation, limitations in date filter precision, restricted access to databases and platforms, non-repeatable supplementary search methods and variability in applying eligibility criteria. We suggest a minimum documentation set specifying the search histories, source-specific exports, record-management information, screening decisions and artificial intelligence (AI)-related records that review teams should preserve and share. We also recommend information-specialist involvement and carefully documented human-in-the-loop AI use. These strategies may not guarantee perfect reproducibility, but they can improve auditability, reduce research waste and strengthen trust in systematic reviews.
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.