Preventive use of a topical anti‐inflammatory glucocorticoid in atopic dogs without clinical sign of otitis does not affect ear canal microbiota and mycobiota
Bibliographic record
Abstract
BACKGROUND: Otitis externa is associated with a lack of bacterial/fungal diversity in atopic dermatitis. Clinical experience has shown that use of topical corticosteroids in the ear canal (EC) can prevent otitis. No data are available on the impact of this treatment on the EC microbiota. HYPOTHESIS/OBJECTIVES: To observe the bacterial/fungal diversity in the EC and the clinical effect of topical corticosteroids administered over a four week period in atopic dogs without active otitis. ANIMALS: Ten atopic dogs without active otitis. METHODS AND MATERIALS: Mometasone was applied in the right EC, while the left was used as control. A clinical and cytological evaluation of the EC was performed. Swabs of each EC were analysed using next-generation sequencing methods. RESULTS: At the beginning of the trial, variations in microbiota and mycobiota were observed between dogs and also within individuals. Statistically, no significant difference was observed in alpha and beta diversity between the treated and the untreated group over time. Clinically, right and left EC diversities were no different at Day (D)28 (P = 0.28). A significant difference was noted between D0 and D28 for the treated ears (P = 0.012) and not for the untreated ears (P = 0.63). No cytological evidence of microbes was found for treated ECs at D28. CONCLUSIONS AND CLINICAL RELEVANCE: These data suggest that the use of topical corticosteroids as proactive treatment is unlikely to increase the risk of secondary microbial overgrowth. The positive clinical effect of this proactive treatment seems to be supported through cytological and otoscopic improvement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".