Incidence of Dermatological Disorders and its Therapeutic Management in Canines
Bibliographic record
Abstract
Incidence of dermatological disorders was studied in canines with clinical ailments. History taking along with skin scraping was performed to examine mites, fungus and bacteria. Out of 914 dogs presented, 52 (5.6%) were affected with skin diseases. Out of 52 positive cases, 27 (51.92%) were positive for demodicosis, scabies (28.85%), atopic dermatitis (7.70%), hot spot (5.76%), fungal dermatitis (1.9%) and mixed dermatitis (1.9%) respectively. Dogs upto 1 year of age group were more prone to skin diseases than older groups. Male (67.31%) suffered more with skin diseases than female (32.69%) dogs. Breed wise predisposition revealed that Spitz were more dominant (26.92%) followed by Labrador (23.08%), Pomerianian (13.46%) and Non-descriptive (13.46%). Regarding the clinical symptoms, overall, 29 positive dogs had itching (55.77%), 9 dogs (17.31%) had scratch (pruritus), 5 dogs (9.62%) were deficient for hair or wool coat (alopecia) and 9 dogs (17.31%) were without clinical symptoms. All the positive cases showed positive response after treatment.
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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".