Accuracy of Carotid Artery Stenosis Quantification with 4-D-Supported 3-D Power-Doppler versus Color-Doppler and 2-D Blood Velocity-Based Duplex Ultrasonography
Bibliographic record
Abstract
Assessment of the severity of internal carotid artery stenosis is relevant to therapeutic decisions. Direct measurement of stenosis in static three-dimensionally rendered ultrasonographic color-Doppler images after an orientation with 4-D gray-scale views (4D/3D-C-US) was recently observed to be metrically non-inferior to angiography. In the study described here, power-Doppler (Christian Doppler was a physicist) ultrasonography (4D/3D-P-US) was prospectively compared with angiography, 4D/3D-C-US and 2-D duplex ultrasonography (DUS) in a similar fashion using blinded observers. Percentage stenosis was measured in 36 patients. Continuous percentage stenosis measures (standard deviation of difference and concordance correlation coefficient) between angiography and 4D/3D-P-US did not differ from the results between angiography observers (p > 0.05). Dichotomous diagnosis with 4D/3D-P-US resulted in κ values similar to the inter-rater agreement of angiography and the inter-method agreement of 4D/3D-C-US and DUS compared with angiography. Binary accuracy did not differ between 4D/3D-P-US, 4D/3D-C-US and DUS (p > 0.5). In conclusion, stenosis grading using 4D/3D-P-US exhibited non-inferior inter-method agreement with angiography at good accuracies, similar to 4D/3D-C-US and DUS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".