Bibliometric Analysis of Surgical Articles Using Bayesian Statistics
Why this work is in the frame
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Bibliographic 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.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.178 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.012 |
| Bibliometrics | 0.206 | 0.500 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it