Fluoroquinolones and glucocorticoids as risk factors for cranial cruciate ligament disease in Retrievers
Bibliographic record
Abstract
Objective This study aimed to determine if a higher proportion of Retrievers with cranial cruciate ligament disease (CCLD) had previous systemic exposure to fluoroquinolones or glucocorticoids compared to Retrievers without CCLD. Animals Client-owned, Labrador and Golden Retrievers and Retriever crosses, aged 2–12 years old, were enrolled from Kansas State University and Washington State University with CCLD (cases) and without CCLD (nCCLD; healthy controls). Methods The study was designed as a retrospective, multi-institutional case–control study. Medical records (2019–2023) were reviewed for systemic and topical exposure to fluoroquinolones or glucocorticoids within 6 months of CCLD exam or visit (nCCLD) or anytime throughout life prior to injury or visit. Data were analyzed using univariate and multivariate logistic regression analyses. The results were reported as coefficients, standard errors, p-values, and odds ratios with 95% confidence intervals. A p-value of <0.05 was considered significant. Results A total of 419 dogs (216 cases and 203 controls) were enrolled in this study. The odds ratios of CCLD after systemic fluoroquinolone exposure were three times (CI: 1.01–8.90) higher than the odds ratios of CCLD without fluoroquinolone exposure. The odds ratios of CCLD after systemic glucocorticoid exposure were 3.51 times (CI: 1.94–6.37) higher than the odds ratios of CCLD without glucocorticoid exposure. Topical administration was not found to pose the same risk. Conclusion The findings of this study suggest that the administration of systemic fluoroquinolones or systemic glucocorticoids was identified as a risk factor for the development of CCLD in Retriever breeds. Prospective studies are needed. Clinical relevance Although not all risk factors for CCLD can be mitigated, systemic fluoroquinolones and/or glucocorticoids should be used cautiously in Retriever breeds.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".