Assessing the sensitivity of human skin hyperspectral responses to increasing anemia severity levels
Bibliographic record
Abstract
Anemia is a prevalent medical condition that seriously affects millions of people all over the world. In many regions, not only its initial detection but also its monitoring are hindered by limited access to laboratory facilities. This situation has motivated the development of a wide range of optical devices and procedures to assist physicians in these tasks. Although noticeable progress has been achieved in this area, the search for reliable, low-cost, and risk-free solutions still continues, and the strengthening of the knowledge base about this disorder and its effects is essential for the success of these initiatives. We contribute to these efforts by closely examining the sensitivity of human skin hyperspectral responses (within and outside the visible region of the light spectrum) to reduced hemoglobin concentrations associated with increasing anemia severity levels. This investigation, which involves skin specimens with distinct biophysical and morphological characteristics, is supported by controlled in silico experiments performed using a predictive light transport model and measured data reported in the biomedical literature. We also propose a noninvasive procedure to be employed in the monitoring of this condition at the point-of-care.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".