Cardiology–cardiothoracic subspeciality training in South Africa: a position paper of the South Africa Heart Association
Bibliographic record
Abstract
Over the past decades, South Africa has undergone rapid demographic changes, which have led to marked increases in specific cardiac disease categories, such as rheumatic heart disease (now predominantly presenting in young adults with advanced and symptomatic disease) and coronary artery disease (with rapidly increasing prevalence in middle age). The lack of screening facilities, delayed diagnosis and inadequate care at primary, secondary and tertiary levels have led to a large burden of patients with heart failure. This leads to suffering of the patients and substantial costs to society and the healthcare system. In this position paper, the South African Heart Association (SA Heart) National Council members have summarised the current state of cardiology, cardiothoracic surgery and paediatric cardiology reigning in South Africa. Our report demonstrates that there has been minimal change in the number of successfully qualified specialists over the last decade and, therefore, a de facto decline per capita. We summarise the major gaps in training and possible interventions to transform the healthcare system, dealing with the colliding epidemic of communicable disease and the rapidly expanding epidemic of non-communicable disease, including cardiac disease.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".