In silico investigation of the effects of hemolysis on the hyperspectral absorptance of blood in motion
Bibliographic record
Abstract
Measurement of the optical absorptance of blood can provide insight into its composition and behaviour. Accordingly, optical devices and sensors are commonly used in a clinical setting to measure the absorptance of blood, either directly or indirectly through measurement of skin spectral responses. These measurements enable the evaluation or constant monitoring of a patient's blood. In this paper, we perform predictive simulations to investigate the absorptance of blood and how it is affected by hemolysis. These simulations are performed using a cell-based light interaction model, known as CLBlood, which accounts for the orientation and distribution of red blood cells. This allows us to evaluate the effect of hemolysis under different flow conditions. Furthermore, we produce results in the ultraviolet, visible and infrared domains using CLBlood's hyperspectral capabilities. We then evaluate the sensitivity of the absorptance signature of blood to hemolysis in each of these domains under several experimental conditions. The observations in this paper enhance our understanding of the impact of hemolysis on the optical absorptance of blood, potentially leading to simplified and more accurate methods for its detection and monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".