ArticleBMC medical research methodology2026
Bibliometric, methodological and reporting characteristics of systematic reviews with explicit AI disclosure statements: an exploratory meta-research study.
Article in BMC medical research methodology, 2026. 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.
- Trends in Volume, Timeliness, and Reporting Quality of Oncology Systematic and Scoping Reviews: A 25-Year Analysis.Current oncology (Toronto, Ont.) · 2026Article
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
No grant is acknowledged in the PubMed record.
Abstract
introductionThe exponential increase in systematic reviews (SRs), accelerated by LLM-based generative AI and non-LLM automation tools, risks redundancy, overlap, and research waste. However, there is limited empirical evidence on how SRs that disclose AI use apply and report these tools in practice, including the extent of transparency and validation.
aimTo assess the methodological and reporting features of SRs that explicitly acknowledge LLM-based and non-LLM automation tools’ use in a dedicated statement, and to examine how these features relate to bibliometric characteristics of these SRs.
methodsAn exploratory, cross-sectional, meta-research study with individual SRs as the unit of analysis. A random sample was drawn from a purposively defined stratum, comprising only SRs with designated AI statements. Screening was conducted by a single researcher; data extraction was performed by one researcher and independently verified by four others. Descriptive analyses were supplemented by Wilcoxon rank-sum tests, Spearman’s ρ, and χ² tests.
resultsWe included 188 SRs; 75% reported using LLMs, and in 92% of studies LLM-based and non-LLM automation tools were used for manuscript writing. Reviews with designated AI statements were predominantly published in Elsevier or Elsevier-supported journals (70.2%). Only 42% referenced a pre-registered protocol; the median time from protocol registration to first journal submission was 267 days. Reviews with more included studies were published in higher-impact journals (ρ = 0.34, p < 0.0001), as were reviews led by authors affiliated with high-income countries (W = 1931.5, p < 0.0001). Reviews with more authors were more likely to have a pre-registered protocol (χ² = 20.54, p < 0.0001), and pre-registered reviews more often adhered to a reporting checklist (χ² = 8.93, p = 0.0027).
conclusionsLLM-based and non-LLM automation tools were used predominantly for writing. Sharing of prompts and human-validation procedures was insufficient, and many reviews exhibited methodological and reporting weaknesses. Clearer guidance is needed to support transparent, rigorous use of LLM-based and non-LLM automation tools in SRs.
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.