Association between excess body weight and urine protein concentration in healthy dogs
Bibliographic record
Abstract
BACKGROUND: Markedly overweight people can develop progressive proteinuria and kidney failure secondary to obesity-related glomerulopathy (ORG). Glomerular lesions in dogs with experimentally induced obesity are similar to those in people with ORG. OBJECTIVES: The aim of this study was to evaluate if urine protein and albumin excretion is greater in overweight and obese dogs than in dogs of ideal body condition. METHODS: Client-owned dogs were screened for underlying health conditions. These dogs were assigned a body condition score (BCS) using a 9-point scoring system. Dogs with a BCS of ≥ 6 were classified as being overweight/obese, and dogs with a BCS of 4 or 5 were classified as being of ideal body weight. The urine protein:creatinine ratio (UPC) and urine albumin:creatinine ratio (UAC) were then determined, and compared between 20 overweight/obese dogs and 22 ideal body weight control dogs. RESULTS: Median UPC (0.04 [range, 0.01-0.14; interquartile range, 0.07]) and UAC (0.41 [0-10.39; 3.21]) of overweight/obese dogs were not significantly different from median UPC (0.04 [0.01-0.32; 0.07]) and UAC (0.18 [0-7.04; 1.75]) in ideal body weight dogs. CONCLUSIONS: Clinicopathologic abnormalities consistent with ORG were absent from overweight/obese dogs in this study.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".