The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023)
Bibliographic record
Abstract
AMA Zdzierak B, Krawczyk-Ożóg A, Dziewierz A. The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023). Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2024;20(2):201-205. doi:10.5114/aic.2024.140904. APA Zdzierak, B., Krawczyk-Ożóg, A., & Dziewierz, A. (2024). The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023). Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 20(2), 201-205. https://doi.org/10.5114/aic.2024.140904 Chicago Zdzierak, Barbara, Agata Krawczyk-Ożóg, and Artur Dziewierz. 2024. "The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023)". Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej 20 (2): 201-205. doi:10.5114/aic.2024.140904. Harvard Zdzierak, B., Krawczyk-Ożóg, A., and Dziewierz, A. (2024). The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023). Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 20(2), pp.201-205. https://doi.org/10.5114/aic.2024.140904 MLA Zdzierak, Barbara et al. "The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023)." Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, vol. 20, no. 2, 2024, pp. 201-205. doi:10.5114/aic.2024.140904. Vancouver Zdzierak B, Krawczyk-Ożóg A, Dziewierz A. The gender gap in interventional cardiology research: insights from Advances in Interventional Cardiology journal (2015-2023). Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2024;20(2):201-205. doi:10.5114/aic.2024.140904.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".