COMPARISON OF SONOGRAPHIC FEATURES OF BENIGN AND NEOPLASTIC DEEP LYMPH NODES IN DOGS
Bibliographic record
Abstract
The differentiation of benign vs. neoplastic lymph nodes impacts patient management. Specific sonographic features are typically considered when assessing lymph nodes in dogs. However, the usefulness of these criteria in distinguishing benign vs. malignant lymph nodes remains largely unknown, especially for deep lymph nodes. Our aim was to compare sonographic features in benign and neoplastic deep lymph nodes with the hope of identifying predictive criteria. Thirty-one deep lymph nodes (16 mesenteric, 10 medial iliac, three hepatic, one sternal, and one cranial mediastinal) in 31 dogs were examined prospectively with B-mode and Color flow Doppler. Lymph nodes were aspirated using ultrasound-guidance and final diagnosis were established based on cytologic and/or histopathologic interpretation. Prevalence of each sonographic feature and combinations of two features was calculated for each group and compared using a χ(2) -test or Student's t-test for unequal variances. Ten lymph nodes were benign (hyperplastic and/or inflammatory) and 21 were neoplastic. All were hypoechoic, except for one neoplastic lymph node. Maximal short-axis diameter (P=0.0006) and long-axis diameter (P=0.01), and SA/LA ratio (P=0.008) were increased significantly for neoplastic (2.8, 5.5 cm, and 0.50, respectively) vs. benign (1.2, 3.8 cm, and 0.34, respectively) lymph nodes. The prevalence of other features was similar between groups. Doppler evaluation was possible in 77% of lymph nodes, but there was no significant difference between groups. When any two ultrasound features were combined, the only difference between benign and neoplastic lymph nodes was for the combination of contour regularity and appearance of the perinodal fat (P=0.03).
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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".