What is your diagnosis? Ventral neck mass in a dog
Bibliographic record
Abstract
: A 14-year-old male Labrador Retriever was presented for lethargy and collapse. On physical examination, numerous abnormalities were found, including a large ventral neck mass (100 cm(3)) in the area of the thyroid gland. Fine-needle aspirates revealed 2 apparent populations of cells: one suspected to be a well-differentiated thyroid carcinoma, and the other consisting of large pleomorphic to spindloid cells suggestive of sarcoma. Two days later, the dog died at home. A full necropsy was not performed, but examination of the head and neck revealed a well-encapsulated mass adjacent to the cranial trachea and larynx. A section of the mass was evaluated histologically and a diagnosis of anaplastic thyroid carcinoma was made. Immunohistochemical evaluation with antibodies to thyroglobulin, cytokeratin, and vimentin confirmed distinct populations of malignant epithelial and malignant mesenchymal cells, and the diagnosis was amended to thyroid carcinosarcoma. Thyroid carcinosarcoma is a rare neoplasm in dogs in which the cell type comprising the mesenchymal component can vary. Immunochemistry to demonstrate the 2 cell types may be necessary to differentiate thyroid carcinosarcoma from anaplastic thyroid carcinoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".