Modeling changes in the hemoglobin concentration of skin with total diffuse reflectance spectroscopy
Bibliographic record
Abstract
The ability to monitor changes in the concentration of hemoglobin in the blood of the skin in real time is a key component to personalized patient care. Since hemoglobin has a unique absorption spectrum in the visible light range, diffuse reflectance spectroscopy is the most common approach. Although the collection of the diffuse reflectance spectrum with an integrating sphere (IS) has several calibration challenges, this collection method is sufficiently user-friendly that it may be worth overcoming the initial difficulty. Once the spectrum is obtained, it is commonly interpreted with a log-inverse-reflectance (LIR) or “absorbance” analysis that can only accurately monitor changes in the hemoglobin concentration when there are no changes to the nonhemoglobin chromophore concentrations which is not always the case. We address the difficulties associated with collection of the diffuse reflectance spectrum with an IS and propose a model capable of retrieving relative changes in hemoglobin concentration from the visible light spectrum. The model is capable of accounting for concentration changes in the nonhemoglobin chromophores and is first characterized with theoretical spectra and liquid phantoms. The model is then used in comparison with a common LIR analysis on temporal measurements from blanched and reddened human skin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".