Comparative aspects of targeted sentinel lymph node mapping in veterinary and human medicine: opportunities for future research
Bibliographic record
Abstract
There is a significant overlap in the genetic, metabolic and epigenetic alterations between human and companion animal cancers, including those of the oral cavity, breast, bladder, skin, lungs and pancreas. In many cancer types, the identification and removal of affected lymph nodes are essential for accurate cancer management, including treatment and prognosis. Historically, lymphadenectomy and subsequent radical resection based on regional anatomy, palpation and lymph node aspirates were considered sufficient; however, modern approaches with sentinel lymph node mapping (SLN) mapping have increased the accuracy of surgical decision-making. Preoperative and intraoperative SLN mapping techniques in veterinary patients parallel those used in human medicine. While many of these techniques are highly successful, the main challenges with current methodologies are their sensitivity and specificity for the presence of cancer, which can be overcome via precision medicine and targeted SLN mapping agents. Given the large population of dogs and cats with cancer, the crossover of knowledge between species can help to deepen our understanding of many of these cancers and can be useful in evaluating new drugs and/or therapies. In this review, we discuss SLN mapping techniques in veterinary medicine and the concept of precision medicine as it relates to targeted SLN mapping imaging agents. The large number of companion animals affected by cancer is an underutilized resource to bridge the translational gap and we aim to provide a reference for the use of dogs and cats as a comparative model for human SLN mapping.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".