Ultrasonic integrated backscatter coefficient profiling of human coronary arteries in vitro
Bibliographic record
Abstract
A theoretical formulation for the profile of the integrated backscatter coefficient (IBC) is derived. This new formulation is based on a theoretical treatment by Chen et al. [1]. It includes correction for the diffraction of the ultrasonic beam and correction for the non-ideal nature of the reference signal. The inclusion of these correction factors permits accurate quantitative profiling of the IBC over the transducer focal zone. Experimental measurements are first performed on well-calibrated vessel-equivalent phantom materials and subsequently on human coronary arteries in vitro. A spherically focused 50.0 MHz f/1.83 transducer is used. IBC profiles are shown for three samples that are representative of early, mid, and advanced atherosclerotic coronary disease. The IBC profiles clearly differentiate the arterial tissues. However, variation between samples with histologically confirmed intimal thickening (N = 24) was large. The mean IBC (+/- 1 standard deviation), in (Sr.mm)-1, for media, adventitia, and thickened intima were 3.86 x 10(-3), 1.53 x 10(-2), and 2.24 x 10(-2), respectively. The mean IBC of thickened intima is larger than previous measurements obtained from femoral arteries, and the mean IBC for media and adventitia layers are lower, reflecting differences in tissue composition between coronary and femoral vessels.
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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 | 0.001 |
| 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) | 0.000 | 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 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".