The use of spatially resolved fluorescence and reflectance to determine interface depth in layered fluorophore distributions
Bibliographic record
Abstract
The possibility of using spatially resolved fluorescence and reflectance measurements to recover tissue optical properties, fluorophore concentration and the thickness of a superficial layer in a two-layer geometry was investigated. A diffusion theory model was used to fit reflectance and fluorescence data generated using Monte Carlo simulations or experimentally obtained using tissue-simulating phantoms. Initial analysis fitting diffusion theory generated data suggested that it should be possible to recover all parameters from a single set of spatially resolved fluorescence and reflectance measurements. However, when Monte Carlo or experimental data were fitted the results were less impressive. Overall, it was shown that there is a strong coupling between interface depth, fluorophore concentration and tissue absorption, especially at larger depths. The recovery of all input parameters from a single set of spatially resolved measurements was limited to interface depths less than 3 mm, which is a reasonable range for measuring fluorophore in skin. When the tissue optical properties and fluorophore concentrations were known, then the interface depth could be monitored with good accuracy in simulated serial measurements. These results may also point to deficiencies in the diffusion theory model that introduce significant errors in the fitted results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".