<i>Otitis externa</i> in Dogs: A Cross-Sectional Analysis of Prevalence and Contributing Risk Factors in Palam Valley of Himachal Pradesh
Bibliographic record
Abstract
The present study was conducted at Palampur from June 2022 to July 2023 to determine the prevalence and epidemiological risk factors of otitis externa in dogs. Out of 2,476 dogs presented, 81 were diagnosed with otitis, showing an overall hospital prevalence of 3.27%. The highest prevalence was observed in Labrador Retrievers (23.45%), followed by German Shepherds (14.81%) and Pomeranians (12.43%). Dogs aged 1-5 years showed the highest prevalence (41.97%). Male dogs were more frequently affected (80.25%) compared to females (19.75%). The incidence peaked during the rainy season (59.25%). Clinical signs included pruritus, head shaking, ear pain, swelling, and purulent discharge. Findings highlight breed, age, sex, and season as significant risk factors for otitis externain dogs in Palam valley. Managing otitisin dogs needs regular medicalcheck-ups and client education.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".