Prospective assessment of canine thyroid cancer—part I: nodal metastatic rate and impact of nodal immunohistochemistry in 70 dogs
Bibliographic record
Abstract
OBJECTIVE: To determine the rate of nodal metastasis in dogs with thyroid cancer and evaluate whether immunohistochemistry (IHC) identifies additional metastases beyond evaluation with H&E. ANIMALS: 70 prospectively enrolled client-owned dogs with thyroid cancer managed with thyroidectomy. METHODS: Dogs underwent thyroidectomy with concurrent elective bilateral medial retropharyngeal (MRP) ± deep cervical lymphadenectomy. Thyroid tumors and associated lymph nodes were reviewed by a single board-certified pathologist. Immunohistochemistry was used for all primary tumors (thyroid transcription factor-1 and calcitonin) to support a diagnosis of follicular or medullary carcinoma. Lymph nodes without evidence of metastasis after H&E review were labeled with the antibody associated with the wider uptake in the primary tumor. RESULTS: 77 thyroid cancers were resected from the 70 dogs enrolled, including 61 (79.2%) follicular, 8 (10.7%) medullary, and 7 (9.3%) mixed follicular/medullary carcinomas, with 1 (1.3%) carcinosarcoma. Twelve dogs had evidence of nodal metastasis following H&E review. Occult micrometastasis was identified in 1 dog following nodal IHC, resulting in documented metastasis in 13 of 70 (18.6%) dogs. Metastasis was more common with medullary (5/8) and follicular/medullary carcinoma (3/7) than follicular carcinoma (5/61). All MRP metastases were ipsilateral (7/77 [9.1%]), without contralateral MRP metastases (0/62). Fourteen of 41 (34.1%) deep cervical lymph nodes were metastatic. CLINICAL RELEVANCE: Nodal metastasis was uncommon for follicular carcinoma but was seen in > 50% of dogs with thyroid cancer involving a medullary component. Routine nodal IHC appears to be low yield for thyroid carcinoma. Extirpation of ipsilateral MRP and identifiable deep cervical lymph nodes is recommended with thyroidectomy until detailed preoperative risk stratification becomes available.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".