Neutrophil gelatinase‐associated lipocalin in dogs with chronic kidney disease, carcinoma, lymphoma and endotoxaemia
Bibliographic record
Abstract
OBJECTIVES: To measure serum and urine neutrophil gelatinase-associated lipocalin (NGAL) concentrations in healthy dogs and dogs with chronic kidney disease, neoplasia and endotoxaemia. METHODS: Serum and urine NGAL concentrations were measured in 42 healthy dogs, 11 dogs with chronic kidney disease, 12 dogs with carcinoma, 20 dogs with lymphoma and 15 dogs with lipopolysaccharide-induced endotoxaemia. In dogs with chronic kidney disease, NGAL was measured 3 and 6 months later. RESULTS: Compared with healthy controls, dogs with chronic kidney disease (PÄ0·0008), carcinoma (PÄ0·0072) and lymphoma (PÄ0·0008) had elevated serum and urine NGAL and urine NGAL-to-creatinine ratio. Serum and urine NGAL was not significantly different between dogs with chronic kidney disease, carcinoma or lymphoma (Pê0·12). In dogs with non-progressive chronic kidney disease, NGAL concentrations did not change significantly over the 6-month study period. CLINICAL SIGNIFICANCE: NGAL can be elevated by chronic kidney disease and neoplasia, compared with healthy controls. Further research is needed to determine if uNGAL or uNGAL-to-creatinine ratio is more specific than serum levels to detect chronic kidney disease.
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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 | 0.001 |
| 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".