Win Ratio in Biomedical Science: A Bibliometric Analysis
Bibliographic record
Abstract
Background: The win ratio (WR), introduced in 2012, has emerged as a method to analyze hierarchical composite outcomes by prioritizing clinically significant events, unlike traditional composite time-to-event analyses, which treat events equally. However, use of the WR in biomedical research beyond cardiovascular trials remains unexplored. The study aims to investigate trends in the use of the WR in biomedical research and determine the characteristics of these articles. Methods: Biomedical articles indexed in Web of Science and PubMed were retrieved for 2012-2024. Data extraction included bibliometric information and content details. Statistical analyses utilized descriptive statistics, correlation, and linear regression to assess publication trends and the distribution of WR methodologies across disciplines. Results: A total of 82 studies were analyzed. Publication counts using the WR have grown significantly since its introduction, with an annual compounded growth rate of 30.2%. Most articles were randomized controlled trials (n = 68; 82.9%). Of the 68 randomized controlled trials, 46 (67.6%) were in the field of cardiology. The unmatched WR was the predominant WR approach (n = 57; 69.5%). Mortality was the highest-ranked outcome in most studies (n = 55; 67.1%), and time-to-event variables were the most frequently used across all hierarchical outcome ranks (n = 173). Conclusions: The WR has gained acceptance as a robust and clinically meaningful method for analyzing composite endpoints, particularly for cardiovascular trials. Although challenges remain, its adaptability and ability to prioritize clinically relevant outcomes make it a promising tool for future biomedical research across various disciplines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.112 | 0.362 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.224 | 0.280 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".