Female trailblazers and role models in procedure-based cardiology
Bibliographic record
Abstract
A recent British Junior Cardiologists Association survey demonstrated gender disparity in procedure-based subspecialties within cardiology in the UK.1 Women form between 4.5% and 7.5% of the interventional cardiology (IC) workforce worldwide2 3 and even fewer in electrophysiology (EP): only 6% of female UK cardiology trainees choose EP.1 Interestingly, female trainees were more likely than males to change their preferred subspecialty during early training, away from intervention and EP, in favour of imaging, heart failure and adult congenital heart disease.4 Common elements identified in feedback from potential female cardiology trainees are the shortage of visible female role models and mentors, and concerns about work-life balance.4 5 In this article, four female cardiologists, who are leaders in their chosen procedure-based specialties, discuss their journeys and give advice to all trainees who may be considering an interventional subspecialty. They were each interviewed by trainees from their chosen subspecialty. Dr Rasha Al-Lamee is one of the few female academic interventional cardiologists (IC), and works at Imperial College, London. She completed most of her cardiology training in London, finishing with an interventional fellowship in Milan, Italy, and then a PhD at Imperial College. Rasha combines being a mother, with a fulltime interventional cardiology practice, in addition to a fulfilling academic career. Dr Shazia Hussain is an IC at Glenfield Hospital in Leicester. In addition to cardiology training at Papworth Hospital, she completed a PhD from King’s College London and was awarded the competitive British Cardiovascular Intervention Society Interventional Fellowship in Toronto. Dr Margaret McEntegart is an IC at the Golden Jubilee National Hospital in Glasgow and Honorary Associate Professor of Cardiology at the University of Glasgow. She completed much of her training in Scotland, finishing with an Interventional Fellowship at Columbia University Medical Centre, New York. Margaret is a globally …
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".