ArticleInternational journal of surgery protocols2025
Data extraction error and its implications on systematic reviews in urology: a protocol.
Article in International journal of surgery protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Automated data extraction for systematic reviews using GPT-5.2 and Google Gemini Pro 3: A dual-large language model approach in orthopaedic research.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Assessing data extraction in randomized clinical trials with large language models.BMC medical research methodology · 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
Introduction: For evidence-based healthcare decisions, systematic reviews are essential, yet data extraction errors, often overlooked, pose a substantial threat. In the field of urology, there has been a notable increase in the number of reviews that are not subject to rigorous examination. This study pioneers a shift, investigating data reproducibility issues in urological systematic reviews, highlighting the critical need for scrutiny in evidence synthesis. Methods: This study examines data extraction errors in systematic reviews from 58 urology journals indexed in PubMed and Embase. Systematic reviews that include meta-analyses with randomized controlled trials will be selected. Data extraction will be carried out independently by two reviewers using standardized forms, followed by cross-verification with original sources. Errors will be categorized at the review, meta-analysis, and study levels. Statistical analyses will evaluate the prevalence of these errors and their impact on meta-analytic results. Sensitivity analyses will explore the effect of missing data on the study outcomes. Discussion: This study addresses the often-overlooked issue of data extraction errors in urology systematic reviews, which could impact the reliability of evidence-based decisions. By evaluating the reproducibility of data extraction, the study aims to enhance methodological rigor in urological reviews and improve the validity of conclusions drawn from evidence synthesis. Despite its limitations, this research will contribute valuable insights into the quality of systematic reviews and guide future improvements in evidence-based practice.
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Registered trials
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