Women Leadership in Liver Transplantation—Results of an International Survey
Bibliographic record
Abstract
BACKGROUND: The International Liver Transplantation Society (ILTS) has placed a strong focus on achieving gender equality and equity in liver transplant (LT). We aimed to understand gender distribution in leadership positions among LT physicians around the world and within ILTS. METHODS: In 2019, the ILTS Equality, Diversity, and Inclusion Committee distributed a survey to obtain granular data on gender and characteristics of transplant physicians as well as those in leadership positions in each center. Additionally, data were collected on the gender composition of the ILTS membership, council, chairpersons, and committees and from the United Network for Organ Sharing. RESULTS: Data were collected from 243 transplant centers. Thirty-two (13.2%) had at least 1 woman as the director of LT, chief of transplant surgery, or chief of transplant hepatology. Of the 243 centers, 133 reported the age and gender of the leadership personnel. Women physicians comprised 152 of the 833 transplant surgeons (18.2%) and 298 of the 935 hepatologists (31.9%). Among the 1331 ILTS physician members, 588 (44.2%) provided gender information in their member profiles, and 155 (26.3%) identified themselves as women. Of the 26 ILTS leadership positions, 7 (26.9%) were held by women. CONCLUSIONS: This analysis of worldwide gender distribution in the LT physician workforce showed notable gender disparity in LT leadership around the globe and within the ILTS. These data provide a launching point for promoting and achieving gender equality and equity in LT.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".