Histologic evaluation of mandibular and medial retropharyngeal lymph nodes during staging of oral malignant melanoma and squamous cell carcinoma in dogs
Bibliographic record
Abstract
OBJECTIVE: To assess histologic evaluation of mandibular lymph nodes (MLNs) and medial retropharyngeal lymph nodes (MRLNs) for metastatic disease during tumor staging for dogs with oral malignant melanoma (OMM) and oral squamous cell carcinoma (OSCC). DESIGN: Retrospective multi-institutional study. ANIMALS: 27 dogs with OMM and 21 dogs with OSCC. PROCEDURES: Medical record databases of 8 institutions were searched to identify dogs with OMM or OSCC that underwent unilateral or bilateral extirpation of the MLNs and MRLNs during the same procedure between January 2004 and April 2016. Information extracted from the records included signalment, primary mass location and size, diagnostic imaging results, histologic results for the primary tumor and all lymph nodes evaluated, and whether distant metastasis developed. RESULTS: Prevalence of lymph node metastasis did not differ significantly between dogs with OMM (10/27 [37%]) and dogs with OSCC (6/21 [29%]). Distant metastasis was identified in 11 (41%) dogs with OMM and was suspected in 1 dog with OSCC. The MRLN was affected in 13 of 16 dogs with lymph node metastasis, and 3 of those dogs had metastasis to the MRLN without concurrent metastasis to an MLN. Metastasis was identified in lymph nodes contralateral to the primary tumor in 4 of 17 dogs that underwent contralateral lymph node removal. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated histologic evaluation of only 1 MLN was insufficient to definitively rule out lymph node metastasis in dogs with OMM or OSCC; therefore, bilateral lymphadenectomy of the MLN and MRLN lymphocentra is recommended for such dogs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".