Pilot clinical study of quantitative ultrasound spectroscopy measurements of erythrocyte aggregation within superficial veins
Bibliographic record
Abstract
BACKGROUND: An enhanced inflammatory response is a trigger to the production of blood macromolecules involved in abnormally high levels of erythrocyte aggregation. OBJECTIVE: This study aimed at demonstrating for the first time the clinical feasibility of a non-invasive ultrasound-based erythrocyte aggregation quantitative measurement method for potential application in critical care medicine. METHODS: Erythrocyte aggregation was evaluated using modeling of the backscatter coefficient with the Structure Factor Size and Attenuation Estimator (SFSAE). SFSAE spectral parameters W (packing factor) and D (mean aggregate diameter) were measured within the antebrachial vein of the forearm and tibial vein of the leg in 50 healthy participants at natural flow and reduced flow controlled by a pressurized bracelet. Blood samples were also collected to measure erythrocyte aggregation ex vivo with an erythroaggregometer (parameter S10). RESULTS: W and Din vivo measurements were positively correlated with the ex vivoS10 index for both measurement sites and shear rates (correlations between 0.35-0.81, p < 0.05). Measurement at low shear rate was found to increase the sensitivity and reliability of this non-invasive measurement method. CONCLUSIONS: We behold that the SFSAE method presents systemic measures of the erythrocyte aggregation level, since results on upper and lower limbs were highly correlated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".