The right parasternal window: when Doppler-beam alignment may be life-saving in patients with aortic valve stenosis
Bibliographic record
Abstract
: The need for multiple transducer positions, especially from right parasternal windows, is consistently mentioned in the recommendations for the accurate measurement of peak velocities across a stenotic aortic valve, but yet poorly adopted.We performed a subanalysis of the largest prospective series on the right parasternal acoustic windows in patients with aortic stenosis (330 consecutive) to calculate the degree of misalignment and estimate the potential outcome implication of this often-forgotten approach.The right parasternal view was highly feasible with an average estimated misalignment from the apical view of 14 ± 16 degree; in 10 cases, an estimated misalignment >40 degree. Right parasternal assessment (vs. apical alone) provided a significant reclassification from moderate to severe or even very-severe aortic valve stenosis. Considering a wellestablished survival benefit provided by either percutaneous or surgical valve replacement in patients with severe aortic stenosis the reclassification would result in approximately 1 life-year saved for every 30-35 patients in whom parasternal view were effectively utilized.
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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 | 0.003 |
| 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 | 0.000 |
| 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".