Keratoconjunctivitis sicca in dogs under primary veterinary care in the<scp>UK</scp>: an epidemiological study
Bibliographic record
Abstract
OBJECTIVES: To estimate the frequency and breed-related risk factors for keratoconjunctivitis sicca (KCS) in dogs under UK primary veterinary care. METHODS: Analysis of cohort electronic patient record data through the VetCompass Programme. Risk factor analysis used multivariable logistic regression. RESULTS: There were 1456 KCS cases overall from 363,898 dogs [prevalence 0.40%, 95% confidence interval (CI) 0.38-0.42] and 430 incident cases during 2013 (1-year incidence risk 0.12%, 95% CI 0.11-0.13). Compared with crossbreds, breeds with the highest odds ratio (aOR) for KCS included American cocker spaniel (aOR 52.33: 95% CI 30.65-89.37), English bulldog (aOR 37.95: 95% CI 26.54-54.28), pug (aOR 22.09: 95% CI 15.15-32.2) and Lhasa apso (aOR 21.58: 95% CI 16.29-28.57). Conversely, Labrador retrievers (aOR 0.23: 95% CI 0.1-0.52) and border collie (aOR 0.30: 95% CI 0.11-0.82) had reduced odds. Brachycephalic dogs had 3.63 (95% CI 3.24-4.07) times odds compared to mesocephalics. Spaniels had 3.03 (95% CI 2.69-3.40) times odds compared to non-spaniels. Dogs weighing at or above the mean bodyweight for breed/sex had 1.25 (95% CI 1.12-1.39) times odds compared to body weights below. Advancing age was strongly associated with increased odds. CLINICAL SIGNIFICANCE: Quantitative tear tests are recommended within yearly health examinations for breeds with evidence of predisposition to KCS and might also be considered in the future within eye testing for breeding in predisposed breeds. Breed predisposition to KCS suggests that breeding strategies could aim to reduce extremes of facial conformation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".