Computation of the Coefficients of the Power law model for Whole Blood and Their Correlation with Blood Parameters
Bibliographic record
Abstract
<p class="1Body">This study introduces a quantitative analysis of the coefficients of the power law model, which is used to describe the non-Newtonian behavior of blood. Twenty blood samples from healthy donors were used to measure the whole blood viscosity under different values of the shear rates, which are between 2.25 and 450.0 s<sup>-1</sup>. The shear rate viscosity curves were used to calculate <em>n</em> (flow index) and <em>m</em> (the consistency of the fluid) according to the power law model. Strong correlations (R<sup>2 </sup>&gt; 0.5) between <em>m</em> and the hematocrit (HCT %), hemoglobin (Hb), erythrocytes count (RBC), mean corpuscle volume (MCV), and mean corpuscle hemoglobin concentration (MCHC) were obtained. Strong correlations (R<sup>2 </sup>&gt; 0.5) between <em>n</em> and the RBC, MCV, and MCHC were achieved. The relation obtained between the power law coefficients and the blood parameters in the present investigation provides new parameters that can be used to evaluate the flow state of blood besides blood viscosity. In addition, these parameters may be used to examine blood under pathological conditions, representing a new tool for the diagnosis of blood abnormal conditions.</p>
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".