Queratoconjuntivitis Seca en caninos de un barrio de la ciudad de Managua
Bibliographic record
Abstract
Keratoconjunctivitis Sicca in canines is one of the most underdiagnosed pathologies, a problem that is most accentuated in countries such as Nicaragua, where veterinary clinical practice is still incipient, the objective of this study was to determine the prevalence of Keratoconjunctivitis Sicca, applying the Schirmer test in 28 multi-breed dogs, which were studied during a day in a neighborhood of the city of Managua. Seven positive cases (25%, CI 95%: 7.17-4.82) of unilateral Keratoconjunctivitis Sicca were obtained, among them the Creole race 4/10, Pitbull 2/7, Chow Chow 1/2, while in the races Dóberman, French Poodle, Siberian Husky, Labrador, German Shepherd and Pekingese no positivity was found, sex was not a predisposing factor since in females they were positive 2/13 and in males 5/15 (p≥0.05), the average tear film in young dogs was 18.4 mm and in adults it was 21.0 mm showing significant difference (p = 0.049). This study highlights the need to include in the daily clinic the ophthalmological check-up in canines with complementary tests for the early detection of Keratoconjunctivitis Sicca.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".