Clinical practice recommendations and expected outcomes with fluorescent light energy: a Delphi-like consensus
Bibliographic record
Abstract
Abstract Background Fluorescent Light Energy (FLE) is a promising alternative to systemic therapies in veterinary dermatology and surgery for managing skin conditions and improving the quality of life of animals and their owners. Hypothesis/objectives In the absence of specific recommendations for FLE use, an international DELPHI consensus research project was conducted to establish best practices. Methods An international Steering Committee (SC) of a board-certified veterinary surgeon and veterinary dermatologists combined a literature review with clinical expertise to create recommendations. General practitioners and veterinarians of various specialties were selected to review and vote on the recommendations. Votes were collected electronically, independently, and anonymously. Results The statements covering the following topics were analyzed in this paper: (i) Understanding photobiomodulation via FLE; (ii) Indications and Protocols for FLE; and (iii) FLE pet owner information. Consensus was reached on 33 out of 33 statements (100%) addressing the use of photobiomodulation via FLE; the practical modalities of FLE as monotherapy or adjunct therapy; healing biological benefits of photobiomodulation; reduction of antibiotic use in the management of bacterial skin infections; clinical indications where FLE can show the most favorable results along with protocols and duration of treatment; and communication with animal owners on safety measures and FLE’s benefits for their animal. Conclusions and clinical importance This consensus provides practical guidelines on the utilization, application, and benefits of FLE when addressing veterinary dermatological conditions. It contributes to optimizing animal and owner welfare and bridges the gap between expert recommendations and the real-life experiences of general practice veterinarians.
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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 | 0.317 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".