Validation of a new ultrasound method for the measurement of carotid artery intima medial thickness and plaque dimensions.
Bibliographic record
Abstract
BACKGROUND: Carotid ultrasound is an accepted method for the detection of subclinical atherosclerosis. Valid methods that allow quantitation of carotid atheroma burden may be useful for stratifying risk. OBJECTIVE: To validate the results of intima medial thickness (IMT) and plaque measurements using a newly created software algorithm by comparing them with those obtained using a previously validated method. METHODS: Carotid ultrasound videotapes (n=24) were analyzed by experienced observers using a validated method and a new method. Ultrasound parameters were compared by measuring the difference +/- SD to yield indexes of accuracy and precision. Performance was also assessed using correlation and Bland-Altman analyses. RESULTS: Average IMT (n=24), plaque area (n=46), and several indexes that integrate IMT and plaque measurements were all found to be comparable with measurements obtained using the previously validated method. For example, the plaque area showed excellent accuracy and precision (-0.17+/-2.0 mm2, P=0.56), excellent correlation (r=0.98, standard error of the estimate = 2.01 mm2, P<0.001) and no evidence of bias using Bland-Altman analyses (Spearman's rho = 0.04, P=0.82). CONCLUSIONS: A new algorithm for the quantitation of carotid atheroma burden yields results that are comparable with those of a previously validated and widely used method. Availability of valid tools for measuring carotid ultrasound should facilitate the incorporation of this procedure into clinical risk stratification paradigms.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".