A Phantom-Free Approach for Estimating the Backscatter Coefficient of Aggregated Red Blood Cells Applied to COVID-19 Patients
Bibliographic record
Abstract
The ultrasound backscatter coefficient (BSC) is a frequency-dependent quantity intrinsic to biological tissues that can be recovered from backscattered radio frequency (RF) signals, granted acquisitions on a reference phantom (RP) are available under the same system's settings. A phantom-free (PF) BSC estimation method is proposed based on Gaussian-shaped approximation of the point spread function (PSF) (electronics and piezoelectric characteristics of the scanner's probe) and the effective medium theory combined with the structure factor model (EMTSFM), albeit the proposed approach is amenable to other models. Meanwhile, the total attenuation due to intervening tissues is refined from its theoretical value, which is based on reported average behaviors of tissues, while allowing correction for diffraction due to the probe's geometry. The RP method adapted to a similar approach except for the Gaussian approximation is also presented. The proposed PF and RP methods were compared on ten COVID-19 positive patients and 12 control subjects with measures on femoral veins and arteries. In this context, red blood cells (RBCs) are viewed as scatterers that form aggregates increasing the backscatter under the COVID-19 inflammatory condition. The considered model comprises five parameters, including the mean aggregate size estimated according to the polydispersity of aggregates' radii, and anisotropy of their shape. The mean aggregate size over the two proposed methods presented an intraclass correlation coefficient (ICC) of 0.964 for consistency. The aggregate size presented a significant difference between the two groups with either two methods, despite the confounding effect of the maximum Doppler velocity within the blood vessel and its diameter.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".