Epidemiology of valvular heart disease in France
Bibliographic record
Abstract
BACKGROUND: Demographic changes and improvements in the diagnosis and treatment of valvular heart diseases (VHDs) have led to changes in its epidemiological profile. AIMS: To describe the epidemiology of VHD in France in 2022. METHODS: Adults hospitalized due to VHD in 2022 were identified from the French National Health Data System and categorized by type of VHD on the basis of hospital diagnoses and interventions. Incidence and prevalence rates were calculated using national French demographic data. RESULTS: In 2022, 51,894 adults (60.1% men) were hospitalized for VHD (97.0/100,000 inhabitants). The most frequently observed hospitalized VHDs were AS (61.6%) and MR (23.2%). The mean age at hospitalization was 74.0years, and this was higher for AS than MR (77.3 vs 71.2years). Infectious endocarditis was managed during the index hospitalization in 13.3% of patients. During the index hospitalization and the following 6months, 75.0% of patients underwent valve repair or replacement. Among hospitalized patients with AS, 56.9% had transcatheter aortic valve implantation and 24.9% had surgical aortic valve replacement. Among patients hospitalized for MR, 27.1% underwent surgical mitral valve repair, 12.7% transcatheter mitral valve repair and 19.1% mitral valve replacement. The all-cause death rate 1year after hospitalization for VHD was 13.7%. Overall, in France, on 1 January 2023, 1.90% of the adult population had VHD (2.08% of men and 1.72% of women). Overall, 363,574 had aortic stenosis (AS) and 409,570 had mitral regurgitation (MR). CONCLUSION: VHDs are a major burden in France, particularly degenerative valve diseases of the left heart in older adults.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".