ReviewAnnals of surgery open : perspectives of surgical history, education, and clinical approaches2025
Bibliometric Analysis of Surgical Articles Using Bayesian Statistics.
Review in Annals of surgery open : perspectives of surgical history, education, and clinical approaches, 2025. 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.
- Robotic-assisted cardiothoracic surgery: a comprehensive bibliometric analysis of trends, innovations, and future directions.Journal of cardiothoracic surgery · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The study aims to investigate the landscape and trends in the use of Bayesian statistics in surgical papers published in high-impact journals over the past 2 decades, determine the characteristics of these papers, and assess the quality of Bayesian analysis reporting. Background: Observational and clinical trials have traditionally employed frequentist approaches. Bayesian framework enables the incorporation of prior evidence, flexible modeling of uncertainty, and returns a direct probabilistic summary of the estimates of interest that can provide valuable insight. However, their use in high-impact surgical research remains underexplored. Methods: Surgical articles from high-impact surgical and medical journals indexed in Web of Science and PubMed were retrieved for the period from January 2000 to August 2024. Data extraction covered bibliometrics and content details. The Reporting of Bayes Used in Clinical Studies scale (ROBUST) was used to assess Bayesian reporting quality. Results: A total of 120 articles were analyzed. The use of Bayesian statistics in surgical research has increased over time (compounded annual growth rate: 12.3%). General surgery (N = 39, 32.5%) and cardiothoracic surgery (N = 20, 16.7%) were the most represented specialties. The most common study designs were retrospective cohort studies (N = 50, 41.7%), meta-analyses (N = 38, 31.7%), and randomized trials (N = 19, 15.8%). Regression-based methods were the most frequently used (N = 51, 42.5%). The average ROBUST score was 4.1 ± 1.6 out of 7, with 54.0% (N = 54) of studies specifying priors and 29.0% (N = 29) justifying them. Conclusions: Bayesian statistics is increasingly incorporated into surgical research, predominantly observational studies and meta-analyses. However, improvements in the quality and standardization of Bayesian reporting are needed to enhance transparency and reproducibility.
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.