Association between hyperlipidemia and calcium oxalate lower urinary tract uroliths in dogs
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome is associated with formation of calcium oxalate (CaOx) uroliths in humans. OBJECTIVES: To investigate the association between obesity and hyperlipidemia with CaOx lower urinary tract uroliths in client-owned dogs. ANIMALS: Dogs with (n = 55, U [uroliths]-dogs) and without (n = 39, UF [uroliths-free]-dogs) CaOx lower urinary tract uroliths. METHODS: Case-control study. U-dogs were retrospectively enrolled and compared to UF-dogs. Body condition score (BCS; 1-9 scoring scale), serum triglyceride (TG) and total cholesterol (CH) concentrations and glycemia (after >12-hour food withholding) were recorded in both groups. RESULTS: On univariate logistic regression, when excluding Miniature Schnauzers, odds of having uroliths increased by a factor of 3.32 (95% CI 1.38-11.12) for each mmol/L of TG (P = .027), of 39 (95% CI 9.27-293.22) for each mmol/L of glycemia (P < .0001), and of 2.43 (95% CI 1.45-4.45) per unit of BCS (P = .002). In multivariable models, the effect of TG was retained when all breeds were included for analysis and odds of having uroliths increased by a factor of 4.34 per mmol/L of TG (95% CI 1.45-19.99; P = .02). CONCLUSIONS AND CLINICAL IMPORTANCE: Serum lipid screening in dogs diagnosed with CaOx uroliths might be recommended to improve their medical staging and management.
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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 | 0.001 |
| 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) | 0.002 | 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".