Regional and Gender Disparities in Abstracts Presented at the International Transplant Congresses
Bibliographic record
Abstract
BACKGROUND: Women and authors from low- and middle-income countries are notably underrepresented in academia. The intersection of these 2 factors is poorly quantified. We aimed to characterize gender disparities by region through analyzing abstracts presented at the Transplantation Society's congresses. METHODS: Abstracts published in the supplements of the Transplantation journal were included (2016-2022). We used Genderize.io, a predictive algorithm, to classify the first and last authors' gender. Gender disparity was compared by the income level of the author's country of affiliation and the gender inequality index (GII), a composite metric with high scores representing higher levels of gender inequality. RESULTS: Of the 5005 abstracts analyzed (2259 oral presentations and 2746 posters), the majority emerged from high-income settings (low/lower middle: 7%, upper middle: 22%, and high: 71%). Excluding those for whom gender could not be reliably determined, only 39% of the first authors and 24% of the last authors were women. For 61% of the abstracts, the gender of the first and last authors was concordant, and women's last authorship was associated with a higher likelihood of women's first authorship (adjusted odds ratio: 1.88; 95% confidence interval: 1.62-2.14). Although gender disparity was observed across all income levels and GII scores, the proportion of women first authors declined significantly with lower national wealth (low: 19%, lower-middle: 23%, upper-middle: 42%, and high: 40%, P < 0.001) and higher GII scores ( P < 0.001). CONCLUSIONS: Our findings suggest that lack of resources and systemic gender inequities likely limit the progress and career development of women and researchers from low- and middle-income countries in transplantation globally. A deeper understanding of factors contributing to these disparities is needed.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".