P6C-10 Evaluation of the Structure Factor Size Estimator (SFSE) with Simulated Ultrasonic Backscattered Signals from Blood
Bibliographic record
Abstract
Ultrasonic backscattered signals from blood contain frequency-dependent information that can be used to obtain quantitative parameters reflecting the aggregation state of red blood cells (RBCs). Recently, the structure factor size estimator (SFSE) was developed to solve for two parameters: the packing factor W and mean gyration radius of aggregates Rg, expressed in number of RBCs (Yu and Cloutier 2007, J. Acoust. Soc. Am. 122 645-656). Herein, the SFSE is assessed by computer simulations where deterministic rheology parameters, such as the hematocrit, number of RBCs per aggregate, and mean gyration radius of aggregates, are known. RBC aggregation was simulated at 40% hematocrit and mean gyration radii of clusters ranged from 1.6 (quasi-disaggregated RBCs) to 6.4. US frequencies were varied between 20 and 42 MHz, and the insoniflcation angle was fixed to 90deg (perpendicular to the flow). For the quasi-disaggregated RBCs, the packing factor W evaluated with the SFSE from the simulated signals matched those expected from theory: W was similar to the Perkus Yevick packing factor. For aggregating RBCs, good correlation was obtained between the gyration radii evaluated with the SFSE model and those expected (r2= 0.96). To conclude, this study shows the SFSE ability to estimate blood backscattering properties at a physiological hematocrit of 40% and open the way to parametric imaging (using the SFSE) in abnormal blood conditions promoting thrombosis.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".