Grading of Aortic Valve Stenosis at 64-Slice Spiral Computed Tomography
Bibliographic record
Abstract
PURPOSE: We sought to determine the accuracy of multislice spiral computed tomography (MSCT) for assessing of aortic valve stenosis and to establish threshold values of the planimetric aortic valve orifice area (AVA) that best separate between different grades of stenosis severity. MATERIALS AND METHODS: A total of 202 patients (among them 160 patients with aortic valve stenosis) underwent MSCT, transthoracic echocardiography (TTE) and cardiac catheterization (CATH). Planimetric AVA measurements at MSCT were compared with calculations based on Doppler flow velocity measurements by TTE (using the continuity equation) and pressure gradient measurements by CATH (using the Gorlin formula). RESULTS: Series of AVA measurements correlated well between MSCT and TTE (r = 0.86) and between MSCT and CATH (r = 0.90). However, AVA at MSCT (0.98 +/- 0.47 cm) was significantly larger than AVA at TTE (0.81 +/- 0.36 cm; P < 0.05) and CATH (0.80 +/- 0.39 cm; P < 0.05). For severity grades 0 through IV the AVAs at MSCT were 2.69 +/- 0.75, 1.86 +/- 0.30, 1.48 +/- 0.17, 0.95 +/- 0.20, and 0.68 +/- 0.20 cm, respectively. For separating, the 5 severity grades optimal thresholds at MSCT were 2.1, 1.6, 1.2, and 0.9 cm. Using these adjusted thresholds there was perfect agreement in classification between MSCT and CATH in 156 (77%), but a mismatch by 1 grade in 43 (21.5%) and 2 grades in 3 (1.5%) patients (kappaw = 0.86). CONCLUSION: Planimetric AVA measurements on MSCT allows for an accurate grading of aortic valve stenosis severity. However, AVA measurements on MSCT are usually larger than measurements on TTE and CATH. Consequently, the thresholds for discriminating between different severity grades have to be adjusted in MSCT.
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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 | 0.000 | 0.000 |
| 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".