Characterization of the otic bacterial microbiota in dogs with otitis externa compared to healthy individuals
Bibliographic record
Abstract
BACKGROUND: Otitis externa is a common multifactorial disease in dogs. The diversity of the cutaneous microbiota in dogs appears to decrease in diseased states. However, little is known about the microbiota of the canine ear and how it is altered by disease. HYPOTHESIS/OBJECTIVES: To describe the otic bacterial microbiota in dogs with otitis externa compared to healthy dogs. ANIMALS: Samples were collected from 18 dogs with clinical and cytological evidence of otitis externa, and eight clinically normal dogs without cytological evidence of otitis externa. METHODS AND MATERIALS: sequencing of the V4 hypervariable region of the 16S rRNA gene amplicons was performed. Sequences were processed using the bioinformatics software MOTHUR. RESULTS: Bacteria from 27 different phyla were identified. Affected ears had significantly decreased alpha diversity when compared to healthy ears. Community structure and membership also differed between the two groups. Linear discriminant analysis effect size analysis identified 153 operational taxonomic units (OTUs) that were differentially abundant. Eleven OTUs were over-represented in the affected ears, including Staphylococcus, Pseudomonas and Parvimonas. CONCLUSIONS: The otic bacterial microbiota is much more complex than has been identified with previous culture-based studies; otitis externa is accompanied by broad and complex differences in the microbiota.
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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 | 0.001 |
| 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".