Monte Carlo simulation of light transport through spinal cord and surrounding tissues: impact on spinal cord near-infrared spectroscopy (Conference Presentation)
Bibliographic record
Abstract
Introduction: Implantable near-infrared spectroscopy (NIRS) can improve clinical management of spinal cord injury (SCI) patients by continuously monitoring spinal cord oxygenation. This research aims to model light propagation within the spinal cord and the diffuse reflection from its surface to better understand NIRS measurement characteristics. Methods: Monte Carlo simulations were constructed for porcine and human spinal cord geometries using wavelengths between 650 and 950 nm. Spinal cord morphometrics and oxygenation was varied to evaluate their effects on fluence within the spinal cord as well as the diffuse reflection from it. Results: The data indicated that light scattered by the dura mater tends to escape through cerebrospinal fluid without penetrating the spinal cord, leading to a 10-fold decrease in spinal cord fluence. Conclusions: It is essential to evaluate how optical properties and interactions between tissue layers impact spinal cord NIRS measurements for effective translation from animal models to clinical use.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".