Validation of an automated method for assessing brachial artery endothelial dysfunction.
Bibliographic record
Abstract
BACKGROUND: Endothelial dysfunction is an early finding in diverse vascular diseases and can be measured noninvasively using brachial ultrasound. There is great interest in the potential use of this parameter for assessing interventions or cardiovascular prognosis. Automated image analysis of the ultrasound images would facilitate implementation of such measurements in high throughput clinics and/or large clinical trials. OBJECTIVES: To compare a new method designed to assess brachial artery diameter and percentage diameter changes through automated, beat-by-beat image processing (Brachial Tools, Medical Imaging Applications, USA), with a nonautomated method (Prosound System, Jet Propulsion Laboratory, USA). PATIENTS AND METHODS: Brachial ultrasound tapes from 12 patients undergoing endothelial function assessment using forearm cuff-occlusion, measurement of flow-mediated dilation and responses to nitroglycerin were analyzed by both methods. RESULTS: The correlation between the two systems was excellent for both the measurement of absolute diameters (r=0.995, P<0.001) and percentage diameter changes (r=0.973, P<0.001). The automated method demonstrated no bias compared with the frame-by-frame method and excellent precision (0.07 mm and 1.62 percentage diameter change). CONCLUSIONS: The automated method provides valid data while substantially diminishing analysis time.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".