Authorship diversity in global evidence synthesis in genitourinary oncology: A 1998-2022 analysis of cochrane reviews.
Bibliographic record
Abstract
e23006 Background: Women and individuals from low- and lower-middle-income countries (LMICs) are under-represented in oncology literature, particularly in high-impact publications. LMIC clinicians face multipronged barriers to impactful research. Unlike practice guidelines of oncology societies like ASCO, authors worldwide can potentially contribute to genitourinary (GU) oncology-related Cochrane Reviews, a potentially representative sample of global evidence synthesis efforts in the field. However, the state of authorship diversity here is unknown, which this study sought to determine. Methods: We retrospectively searched the Cochrane Database, using the filter “Topic: Urology”, and extracted authorship data for all reviews related to genitourinary cancers, published until 25 July 2022. We divided authors’ national affiliation into either low- and lower-middle-income countries (LMICs) or non-LMICs based on World Bank 2022 classification. For reviews having collaboratives listed as group authors, we treated the collaborative belonging to one country as a single author, instead of analyzing all collaborators separately to prevent data skew from several included individuals. Given the higher accuracy of manual web-searches for ascertaining gender over algorithmic estimation, we utilized the former to achieve ≥90% ascertainment. We endeavored to capture at least one webpage that demonstrated their gender or pronouns, like institutional profile, and used historical gender conventions. Results: A total of 54 GU oncology-related reviews, co-authored by a total of 324 authors, were included. 53 reviews were published by the Cochrane Urology Group, while one review was published by the Cochrane Incontinence Group. Countries with the highest representation of co-authors were the US (24.1%), UK (25.3%), Germany (23.5%), South Korea (6.8%), Australia (6.2%), Netherlands (3.7%), China (2.8%), Canada (2.5%), Brazil (1.5%). No authors were from LMICs. Gender could be ascertained for 94.14% (N = 305/324) of co-authors. Women made up 27.5% (N = 84/305) of co-authors, 16.0% (N = 8/50) of first authors, and 16.89% (N = 9/53) of corresponding authors. Conclusions: Women authors are better-represented in Cochrane Reviews related to genitourinary cancers compared to urology-specific journals, while LMICs were noted to have no representation. Global capacity-building efforts are warranted for enhancing the involvement of LMIC urologists with evidence synthesis. Equitable authorship representation may help expand both the focus and the utilization of high-impact evidence synthesis literature.
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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 | 0.219 | 0.626 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.106 | 0.119 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".