Hair density response to photobiomodulation in canine alopecia X is measured reliably with an ordinal scale: a randomized, controlled, double-blind study
Bibliographic record
Abstract
Objective: To develop an ordinal hair density score (HDS), determine its inter-rater agreement, and use it in a trial of photobiomodulation as a sole treatment for alopecia X. Methods: A 5-level ordinal HDS system was developed. Four blinded veterinary dermatologists independently graded a 50-image reference set using the HDS. Inter-rater agreement was assessed using the quadratic-weighted Fleiss κ, Brennan-Prediger, and Gwet AC2 coefficients. A double-blind, randomized, controlled trial was performed using a convenience sample of alopecia X dogs recruited based on inclusion and exclusion criteria over 16 months. Photoconverter gels were applied on both alopecic sides of each patient once per week for 8 weeks. One randomly chosen side was exposed to excitatory light (active treatment) but not the other (sham). Skin biopsies were taken from the center of each treated side before and at the end of the study. The images of active and sham sides acquired before the study, at day 50, and at the end of the study were graded using the HDS. Results: Inter-rater agreement coefficients were greater or equal to 0.81. Seven dogs were enrolled, but 1 withdrew after day 50. Hair density score evolved over time in both sides, but the OR of improved HDS increased with time only for the 3 central HDS grades. Histopathology revealed no notable differences between sides and across time. Conclusions: The HDS seems valid and useful in assessing the effect of photobiomodulation on the exposed areas of our alopecia X patients. Clinical Relevance: This novel, easily applicable scale may facilitate the therapeutic monitoring of alopecia in dogs.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".