Higher Acceleration/Ejection Time Ratio Predicts Impaired Outcome in Aortic Valve Stenosis
Bibliographic record
Abstract
BACKGROUND: Acceleration time (AT)/ejection time (ET) ratio is a marker of aortic valve stenosis (AS) severity and predicts outcome in moderate-severe AS. METHODS: and mean gradient <40 mm Hg. Outcome was assessed in Cox regression analyses, and results are reported as hazard ratio and 95% CI. RESULTS: Higher AT/ET ratio was significantly associated with lower systolic blood pressure, lower left ventricular ejection fraction, lower stress-corrected midwall shortening, low flow, and with higher left ventricular mass and higher peak aortic jet velocity. AT/ET ratio ≥0.32 provided the optimal cutoff for predicting incident cardiovascular death and heart failure hospitalization in the total study sample. In patients with low-gradient severe AS, this threshold was >0.32. AT/ET ratio ≥0.32 had a 79% higher risk of cardiovascular death and heart failure hospitalization (hazard ratio, 1.79 [95% CI, 1.20-2.68]). In patients with low-gradient severe AS, AT/ET ratio >0.32 was associated with a 2-fold higher risk of cardiovascular death and heart failure hospitalization (hazard ratio, 2.15 [95% CI, 1.22-3.77]). CONCLUSIONS: In asymptomatic nonsevere AS and low-gradient severe AS, higher AT/ET ratio was associated with increased cardiovascular morbidity and mortality. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00092677.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".