Quantitative ultrasound inflammation biomarker on a COVID-19 cohort
Bibliographic record
Abstract
The aim of the current work was to quantify the red blood cells (RBCs) aggregate size in Covid-19 positive patients at risk of developing vascular thrombosis and compare results with control subjects. For this purpose, the current work relied on the effective medium theory combined with the structure factor model that was proposed for RBCs aggregate size estimation. Ten Covid-19 positive patients and twelve control subjects underwent superficial femoral vein and artery imaging sessions. Ultrasound acquisitions consisted in beamformed I&Q frames for each vessel. Backscatter coefficients (BSCs) were computed with the reference phantom method. The EMTSFM was then applied on BSCs yielding an estimation of the RBCs aggregate size (used as a biomarker), and its ancillary aggregate compactness, whilst the theoretical total attenuation, which is based on published values for dermis, muscles and blood, was refined. Increased RBCs aggregate sizes were observed in virus infected Covid-19 patients in comparison with control subjects. The mean aggregate size over the region of interest within vessel exhibited a statistically significant difference between Covid-19 patients and control subjects for veins (p= 0.0279) and arteries (p=0.0252), based on the rank sum test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".