Bibliographic record
Abstract
Introduction: Female participants are historically underrepresented in clinical research, resulting in a paucity of sex-and gender-based analysis (sgBa).In the context of urology, sgBa is critical for understanding differences in disease pathophysiology, response to treatment, and disparities in access to care.this study aimed to assess the utilization of sgBa in urologic research in the past five years.Methods: all interventional and observational studies published from 2019 to July 2023 in the Journal of urology, canadian urological association Journal, British Journal of urology International, urology, european urology, and Biomed central urology were assessed for eligibility.articles were included if the topic applied to both female and male patients, allowing for the assessment of sgBa.articles published in 2022 and 2023 were assessed based on the 2022 sex and gender equity in research (sager) guidelines checklist.the checklist provides guidance for researchers and journal reviewers/editors to ensure the consideration of sgBa in publications.Results: a total of 4702 original research articles were assessed for eligibility, of which 2998 were excluded.the included articles (n=1704) had a pooled 23 333 793 participants (50% male, 42% female, 8% sex not reported).topics with the highest proportion of articles that did not report sex/gender were urethroplasty (n=73%), congenital (50%), and urolithiasis (22%).the most common reason for exclusion was a sex-specific study topic (n=2217).In 2022/2023, the terms sex and gender were used appropriately in 42% and 44% of articles, respectively.the sager checklist was subdivided by article section, with 13% of titles/abstracts, 6% of introductions, 8% of methods, 28% of results, and 18% of discussions/ conclusions from 2022/2023 articles meeting the criteria.observational studies were more likely to adhere to sager guidelines than interventional studies for all article sections.Conclusions: the present study is the first to investigate the representation of female participants, as well as sgBa in urology research.overall, there were more male than female participants and most studies did not address sager guidelines for sgBa.the inclusion of sgBa and reporting requirements in journals is crucial in promoting equity in urology research and patient care.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.267 | 0.070 |
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".