Randomised study for the Optimal Treatment of symptomatic patients with low-gradient severe Aortic valve Stenosis and preserved left ventricular ejection fraction (ROTAS trial)
Bibliographic record
Abstract
BACKGROUND: The best management of symptomatic patients with low-gradient (LG) severe aortic stenosis (AS) and preserved left ventricular ejection fraction (LVEF) has not been established. The Randomised study for the Optimal Treatment of symptomatic patients with low-gradient severe Aortic valve Stenosis (ROTAS) trial aimed to assess the superiority of aortic valve replacement (AVR) versus medical treatment (MT) in this specific group of AS patients. METHODS: Patients with symptomatic LG severe AS and preserved LVEF (>50%) underwent dobutamine stress echocardiography and/or CT-aortic calcium score to confirm AS severity and were then randomised 1:1 to AVR or MT. The primary endpoint was a composite of overall death and/or cardiovascular hospitalisation. RESULTS: The ROTAS study was stopped early because of insufficient recruitment. In the end, only 52 patients (age 79±7 years; women 54%; NYHA III-IV 27%; median STS score 3.3%) were included in the study. During follow-up (mean: 14±7 months), the primary endpoint occurred in 12 (23%) patients. Compared with MT, AVR was not associated with a significant prognostic benefit (events: 5/26 (19%) vs 7/26 (27%) (HR 0.76, 95% CI 0.24 to 2.39, p=0.63). During follow-up, 11 (42%) patients in the MT group developed class I criteria for AVR or severe symptoms justifying a cross-over to the AVR group. CONCLUSIONS: Because of the small number of included patients and short follow-up the ROTAS trial was underpowered and unable to demonstrate a difference in the study endpoint between treatment arms. In patients in the MT arm, a regular echocardiographic and clinical assessment might be useful to disclose those developing class I indications of AVR or severe AS-related symptoms. TRIAL REGISTRATION NUMBER: NCT01835028.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".