Sentinel lymph node mapping with computed tomography lymphangiography and intraoperative methylene blue peritumoral injection has a high detection rate with moderate agreement in dogs with oral neoplasms
Bibliographic record
Abstract
Objective: To investigate the feasibility of indirect CT lymphangiography (CTL) and intraoperative lymphangiography with methylene blue (IOL-MB) for sentinel lymph node (SLN) mapping in canine oral cancer and to report both agreement between the techniques and accuracy of identifying metastatic lymph nodes (LNs). Methods: This prospective study included 38 client-owned dogs with gross macroscopic or incompletely excised microscopic oral neoplasms. All dogs underwent CTL, IOL-MB, and extirpation of bilateral mandibular and retropharyngeal LNs. The detection rate of SLNs using the combined techniques was evaluated, and agreement between CTL and IOL-MB was assessed. Results: The combined techniques identified all metastatic cases (4 of 4 dogs, 6 of 6 LNs) and achieved an SLN detection rate of 97.4% (37 of 38 dogs), with moderate agreement between modalities (76.8%; κ = 0.481). Nine cases showed discrepancies between the techniques, including 1 involving a metastatic LN. Conclusions: CTL and IOL-MB demonstrated moderate agreement and an excellent detection rate for SLNs. With moderate agreement between modalities, our results suggested that the relationship between mapped LNs and true SLNs is not always straightforward. Clinical Relevance: Results of this study suggested that SLN mapping techniques are most effective when there is no evidence of overt clinical LN metastasis. Employing at least 2 modalities is advisable, as metastasis may impact SLN identification rates depending on the technique utilized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".