CAN SONOGRAPHIC FINDINGS PREDICT THE RESULTS OF LIVER ASPIRATES IN DOGS WITH SUSPECTED LIVER DISEASE?
Bibliographic record
Abstract
While abdominal ultrasound and ultrasound-guided fine-needle aspiration cytology are often combined to help determine the type of liver disease in dogs, little is known about the relationship that may exist between the results of these tests. We hypothesized that specific sonographic findings, or combinations of findings, may predict results of liver ultrasound-guided fine-needle aspiration cytology. Hepatic and extrahepatic sonographic findings were recorded prospectively using a standardized form in 70 dogs with clinically suspected liver disease and in which liver ultrasound-guided fine-needle aspiration cytology was performed. The predictive value of sonographic findings in regard to the category of cytology results was assessed with stepwise logistic regression analysis. Sonographic detection of a hepatic mass (> or = 3cm; risk ratio [RR] 3.83, 95% Wald confidence intervals [95% CI] 2.42-3.93, P = 0.0036), ascites (RR 3.82, 95% CI 1.94-4.28, P = 0.0044), abnormal hepatic lymph node(s) (RR 3.01, 95% CI 1.22-4.88, P= 0.0262), and abnormal spleen (RR 3.26, 95% CI 1.20-3.85, P = 0.0274) were the most predictive of liver neoplasia on cytology. Conversely, sonographic detection of hepatic nodules (< 3cm; RR 1.97, 95% CI 0.95-2.96, P = 0.0666) was most predictive of vacuolar hepatopathy on cytology. In dogs with suspected liver disease, several sonographic findings, alone or combined, are thus predictive of liver ultrasound-guided fine-needle aspiration cytology results. In the light of the fact that ultrasound-guided fine-needle aspiration cytology of the liver has limitations, these predictabilities could influence the selection of diagnostic tests to reach a reliable diagnosis.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".