Light dosimetry for low-level laser therapy: accounting for differences in tissue and depth
Bibliographic record
Abstract
While Low-level Light Therapy (LLLT) has demonstrated efficacy for certain indications, some aspects of the technology are still controversial. Clinical studies on LLLT range from low quality anecdotal studies to blinded, randomized, control clinical studies. These have used a variety of wavelengths, optical powers and variations in other laser parameters. While these studies show a large range in treatment outcome, comparison of treatment efficacy between these studies with respect to light dose is all but impossible since the light dose characterization in the LLLT field has not been properly defined and is not standardized. Surface irradiance is typically used in the LLLT field as the light dose parameter, ignoring factors such as tissue optical properties, beam divergence, pulsing of the source and tissue thickness to the organ or joint of interest. Drawing on experience with light dosimetry for photodynamic and photothermal therapy, we will provide an overview of light transport and dosimetry in tissue and its implications for LLLT dosimetry. In particular, we suggest that the proper measure of dose is the light fluence rate delivered to the organ or tissue of interest, usually several millimeters below the tissue surface. We have developed a technique that provides an estimate of the subsurface fluence rate based on the diffuse reflectance measured at the tissue surface. Using Monte Carlo simulations and measurements on tissue simulating phantoms, we demonstrate that this technique can be used to predict the subsurface fluence rate to within 30% of the actual value at 3-10 mm below the tissue surface.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".