Comparison of mesenteric lymphadenography performed via surgical and laparoscopic approaches in dogs
Bibliographic record
Abstract
OBJECTIVE: To determine whether injection of a mesenteric lymph node with iodinated aqueous contrast medium results in radiographic delineation of the thoracic duct and its branches, ascertain the ideal interval between injection and radiographic imaging, and evaluate mesenteric lymphadenography performed via laparoscopic and surgical approaches in dogs. ANIMALS: 10 adult dogs. PROCEDURE: In each dog, a right paracostal laparotomy or a right laparoscopic approach was performed to identify a mesenteric lymph node for injection of an iodinated aqueous contrast agent (0.22 mL/kg [81.4 mg of iodine/kg]). Lateral radiographic views were obtained at 60, 120, 180, 240, and 300 seconds after injection. RESULTS: A mesenteric lymph node was identified and injected with contrast medium in each dog. Via paracostal laparotomy, lymph node injection resulted in successful lymphangiographic evaluation in 4 of 5 dogs, whereas via the laparoscopic approach, lymph node injection resulted in successful lymphangio-graphic evaluation in 2 of 5 dogs. In successful radiographic evaluations, injected lymph nodes, mesenteric lymphatics, and the thoracic duct and its branches were delineated. Radiographs obtained at 60 and 120 seconds after injection of contrast medium provided the most detail. CONCLUSIONS AND CLINICAL RELEVANCE: Injection of a mesenteric lymph node directly with contrast medium appears to be a feasible technique for delineation of the thoracic duct and its branches in dogs and might be useful in small animals in which mesenteric lymphatic catheterization can be difficult and lymphangiography is more likely to fail. Refinement of the laparoscopic technique may provide a minimally invasive approach to lymphadenography.
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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 | 0.002 |
| 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 | 0.001 |
| 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".