A blinded randomised split‐body clinical trial evaluating the effect of fluorescent light energy on antimicrobial management of canine interdigital furunculosis
Bibliographic record
Abstract
BACKGROUND: Canine interdigital furunculosis (CIF) is a complex, relapsing inflammatory condition, typically complicated by deep bacterial infections requiring prolonged systemic antibiotics. HYPOTHESIS/OBJECTIVES: This split-body study, where dogs acted as their own control, evaluated whether the adjunctive use of fluorescent light energy (FLE) could shorten the time to clinical resolution of CIF and minimise systemic antimicrobial use. ANIMALS: Thirty-five client-owned dogs with signs of interdigital furunculosis in at least two paws. MATERIALS AND METHODS: This prospective, single-blinded, randomised, split-body multicentre clinical trial treated dogs with systemic antibiotics based on bacterial culture and sensitivity. One paw per dog was randomly selected using a coin-toss method for weekly FLE application, while the other paw served as a control. Dogs were scored every 2 weeks over 56 days on two parameters: a global lesion score (including haemorrhagic vesicles, fistulae with draining tracts, crusts and ulcers) and neutrophils engulfing bacteria score (NES, 0-4). Time to clinical resolution and lesion scores were assessed and compared between groups. RESULTS: At Day (D)28 and D56, the FLE group showed significantly more healed paws (50% and 88%, p = 0.021) compared to the control (17% and 54%, p = 0.008). The median time to clinical resolution was shorter for the FLE group (35 days) compared to the control group (56 days, p = 0.017). No difference in NES score was observed between groups. CONCLUSIONS AND CLINICAL RELEVANCE: This blinded, randomised, split-body clinical trial demonstrated that FLE is an effective adjunctive therapy for CIF. It reduces the time to clinical resolution and increases the resolution rate while minimising the need for antibiotics.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".