Facilitated Data Relay and Effects on Treatment of Severe Aortic Stenosis in Europe
Bibliographic record
Abstract
Background Many patients with severe aortic stenosis are referred late with advanced symptoms or inappropriately denied intervention. The objective was to investigate whether a structured communication to referring physicians (facilitated data relay) might improve the rate and timeliness of intervention. Methods and Results A prospective registry of consecutive patients with severe aortic stenosis at 23 centers in 9 European countries with transcatheter as well as surgical aortic valve replacement being available was performed. The study included a 3‐month documentation of the status quo (phase A), a 6‐month intervention phase (implementing facilitated data relay), and a 3‐month documentation of a legacy effect (phase‐B). Two thousand one hundred seventy‐one patients with severe aortic stenoses were enrolled (phase A: 759; intervention: 905; phase‐B: 507). Mean age was 77.9±10.0 years, and 80% were symptomatic, including 52% with severe symptoms. During phase A, intervention was planned in 464/696 (67%), 138 (20%) were assigned to watchful waiting, 8 (1%) to balloon aortic valvuloplasty, 60 (9%) were listed as not for active treatment, and in 26 (4%), no decision was made. Three hundred sixty‐three of 464 (78%) patients received the planned intervention within 3 months. Timeliness of the intervention improved as shown by the higher number of aortic valve replacements performed within 3 months (59% versus 51%, P =0.002) and a significant decrease in the time to intervention (36±38 versus 30±33 days, P =0.002). Conclusions A simple, low‐cost, facilitated data relay improves timeliness of treatment for patients diagnosed with severe aortic stenosis, resulting in a shorter time to transcatheter aortic valve replacement. This effect was mainly driven by a significant improvement in timeliness of intervention in transcatheter aortic valve replacement but not surgical aortic valve replacement. Clinical Trial Registration URL : https://www.clinicaltrials.gov/ . Unique identifier: NCT 02241447.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".