Gender representation among speaking and leader roles at European cardio-thoracic surgical annual meetings
Bibliographic record
Abstract
OBJECTIVES: Our goal was to evaluate gender representation among session leaders and abstract presenters at European cardio-thoracic surgical annual meetings. METHODS: We did a descriptive study of the gender distribution among session leaders and abstract presenters at 2 European cardio-thoracic international meetings from 2017 to 2022. Data from publicly available programmes were used to generate a list of session leaders and abstract presenters. The primary outcome was to evaluate the proportion of female sessions leaders at the annual meetings. Descriptive analyses were performed including the Cochran-Armitage trend test for linear trend of proportions. RESULTS: A total of 1025 sessions of 11 annual meetings of the European Association for Cardio-Thoracic Surgery (EACTS) and the European Society of Thoracic Surgeons were examined. A total of 397 (13.2%) out of 3007 total session leaders and 955 (15.2%) out of 6251 abstract presenters were female. From 2017 to 2022, the proportions of both female session leaders and abstract presenters trended significantly [10.4% to 21.9% (P < 0.001) and 13.7% to 18.3% (P < 0.001), respectively]. The EACTS female members and female meeting attendees significantly increased from 2017 to 2022 [11.1% to 15.9% (P < 0.001) and 23.7% to 26.9% (P < 0.001)], respectively. Most of the women attendees at the EACTS and the European Society of Thoracic Surgeons meetings who were session leaders and speakers came from Germany, Italy, the United Kingdom and the United States. CONCLUSIONS: Women are under-represented compared to men in leadership and speaking roles at European cardio-thoracic surgical annual meetings. In the past few years, an encouraging positive trend over time for female leadership roles has been noted; as a result, the proportion of female society members is represented at the annual meetings. However, a substantial gender gap still exists in leading roles of meeting attendees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".