Non-invasive evaluation of therapeutic response in keloid scar using diffuse reflectance spectroscopy
Bibliographic record
Abstract
The pathogenesis and ideal treatment of keloid are still largely unknown, and it is essential to develop an objective assessment of keloid severity to evaluate the therapeutic response. We previously reported that our diffuse reflectance spectroscopy (DRS) system could assist clinicians in understanding the functional and structural condition of keloid scars. The purpose of this study was to understand clinical applicability of our DRS system on evaluating the scar severity and therapeutic response of keloid. We analyzed 228 spectral data from 71 subjects with keloid scars. The scars were classified into mild (0-3), moderate (4-7) and severe (8-11) according to the Vancouver scar scale. We found that as the severity of the scar increased, collagen concentration and water content increased, and the reduced scattering coefficient at 800 nm and oxygen saturation (SaO2) decreased. Using the DRS system, we found that collagen bundles aligned in a specific direction in keloid scars, but not in normal scars. Water content and SaO2 may be utilized as reliable parameters for evaluating the therapeutic response of keloid. In conclusion, the results obtained here suggest that the DRS has potential as an objective technique with which to evaluate keloid scar severity. In addition, it may be useful as a tool with which to track longitudinal response of scars in response to various therapeutic interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".