Differences in the presentation and management of patients with severe aortic stenosis in different European centres
Bibliographic record
Abstract
BACKGROUND: An investigation into differences in the management and treatment of severe aortic stenosis (AS) between Germany, France and the UK may allow benchmarking of the different healthcare systems and identification of levers for improvement. METHODS: Patients with a diagnosis of severe AS under management at centres within the IMPULSE and IMPULSE enhanced registries were eligible. RESULTS: Data were collected from 2052 patients (795 Germany; 542 France; 715 UK). Patients in Germany were older (79.8 years), often symptomatic (89.5%) and female (49.8%) and had a lower EF (53.8%) than patients in France and UK. Comorbidities were more common and they had a higher mean Euroscore II.Aortic valve replacement (AVR) was planned within 3 months in 70.2%. This was higher (p<0.001) in Germany than France/ UK. Of those with planned AVR, 82.3% received it within 3 months with a gradual decline (Germany>France> UK; p<0.001). In 253 patients, AVR was not performed, despite planned. Germany had a strong transcatheter aortic valve implantation (TAVI) preference (83.2%) versus France/ UK (p<0.001). Waiting time for TAVI was shorter in Germany (24.9 days) and France (19.5 days) than UK (40.3 days).Symptomatic patients were scheduled for an AVR in 79.4% (Germany> France> UK; p<0.001) and performed in 83.6% with a TAVI preference (73.1%). 20.4% of the asymptomatic patients were intervened. CONCLUSION: Patients in Germany had more advanced disease. The rate of intervention within 3 months after diagnosis was startlingly low in the UK. Asymptomatic patients without a formal indication often underwent an intervention in Germany and France.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".