Sensitivity and Specificity of Plasma ALT, ALP, and Bile Acids for Hepatitis in Labrador Retrievers
Bibliographic record
Abstract
BACKGROUND: Biochemical indicators for diagnosing liver disease are plasma alanine aminotransferase activity (ALT), alkaline phosphatase activity (ALP), and bile acid concentration (BA). OBJECTIVES: To determine the sensitivity and specificity of ALT, ALP, and BA for detecting primary hepatitis (PH) in clinically healthy Labrador retrievers and investigate whether ALT and ALP can discriminate between dogs with PH and nonspecific reactive hepatitis (RH). ANIMALS: 191 clinically healthy and 51 clinically ill Labrador retrievers with hepatic histopathology. METHODS: Retrospective study. Medical records were reviewed for ALT, ALP, preprandial BA, liver histopathology, and hepatic copper concentrations. RESULTS: In 64% (122/191) of the clinically healthy Labrador retrievers, hepatic histology revealed inflammatory infiltrates. This frequency might be biased because part of them was included as first-line relatives of dogs with copper-associated hepatitis. Sensitivity of ALT, ALP, and BA in this population for detecting acute hepatitis was 45, 15, and 15%, respectively. For chronic hepatitis, sensitivity was 71, 35, and 13%, respectively. Specificity of ALT, ALP, and BA was >90% for AH, CH, and RH. When increased liver enzymes were present, median ALT was significantly higher in PH cases (312 U/L, range 38-1,369) compared to RH cases (91 U/L, range 39-139) (P < .001). There was no difference in ALP between dogs with a PH and a RH (P = .361). CONCLUSIONS AND CLINICAL IMPORTANCE: Histopathologic abnormalities in the liver were present in the majority of apparent clinically healthy Labrador retrievers. The sensitivity of ALT, ALP, and BA for detecting acute and chronic hepatitis in this population was low. More sensitive biomarkers are needed for early detection of liver disease in apparent clinically healthy dogs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".