The implementation of a streamlined TAVI patient pathway across five European countries: BENCHMARK registry
Bibliographic record
Abstract
BACKGROUND: Benchmark best practices have been shown to streamline the clinical pathway for patients undergoing transcatheter aortic valve implantation (TAVI), but the impact in diverse health systems is unknown. AIMS: We evaluated the impact of Benchmark best practices implementation in Germany, Austria, France, Spain, and Italy. METHODS: International, multicentre registry of severe symptomatic aortic stenosis (AS) patients undergoing TAVI with a balloon-expandable valve, before and after Benchmark best practices implementation. Objectives were to reduce overall and intensive care unit (ICU) length of stay (LoS), and to document 30-day safety. RESULTS: A total of 890 patients were analysed in France, 454 in Spain, 362 in Germany, 300 in Italy, and 176 in Austria. Patients had the highest surgical risk in Germany (EuroSCORE II 6.8 ± 7.3%) and lowest in Spain (3.8 ± 2.6%). Austrian patients reported higher rates of prior myocardial infarction, severe pulmonary hypertension, and aortic valve-related symptoms at baseline. After the implementation of Benchmark best practices, the median hospital LoS was significantly reduced in France (5 vs. 3 days, p < 0.001), Spain (6 vs. 4, p < 0.001), Germany (9 vs. 6, p < 0.001), and Italy (7 vs. 5, p < 0.001); reductions in median ICU LoS were reported in France (1.1 vs. 0 days, p < 0.001), Spain (1.9 vs. 1, p < 0.001), and Germany (1 vs. 0.9, p = 0.004). Across all countries, 30-day safety outcomes were uncompromised and reduced rates of major vascular complications rates were observed in Germany (5.9 vs. 0.0%, p < 0.001). CONCLUSION: The implementation of Benchmark best practices in diverse European healthcare systems resulted in reduced hospital and ICU LoS without compromising patient safety. TRIAL REGISTRATION: ClinicalTrials.gov NCT04579445, September 28th, 2020.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".