Educational needs and application of guidelines in the management of patients with mitral regurgitation. A European mixed-methods study
Bibliographic record
Abstract
Aims: To assess the knowledge and application of European Society of Cardiology (ESC) Guidelines in the management of mitral regurgitation (MR). Methods and results: A mixed-methods educational needs assessment was performed. Following a qualitative phase (interviews), an online survey was undertaken using three case scenarios (asymptomatic severe primary MR, symptomatic severe primary MR in the elderly, and severe secondary MR) in 115 primary care physicians (PCPs), and 439 cardiologists or cardiac surgeons from seven European countries. Systematic cardiac auscultation was performed by only 54% of clinicians in asymptomatic patients. Cardiologists appropriately interpreted echocardiographic assessment of mechanism and quantification of primary MR (≥75%), but only 44% recognized secondary MR as severe. In asymptomatic severe primary MR with an indication for surgery, 27% of PCPs did not refer the patient to a cardiologist and medical therapy was overused by 19% of cardiologists. In the elderly patient with severe symptomatic primary MR, 72% of cardiologists considered mitral intervention (transcatheter edge-to-edge valve repair in 72%). In severe symptomatic secondary MR, optimization of medical therapy was advised by only 51% of PCPs and 33% of cardiologists, and surgery considered in 30% of cases (transcatheter edge-to-edge repair in 64%). Conclusion: Systematic auscultation is underused by PCPs for the early detection of MR. Medical therapy is overused in primary MR and underused in secondary MR. Indications for interventions are appropriate in most patients with primary MR, but are unexpectedly frequent for secondary MR. These gaps identify important targets for future educational programs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".