Bibliographic record
Abstract
The kidney cancer’s thyroid metastases are very rare. The renal cancer is called the tumor of the internist. The best known of this cancer metastatic sites are the bone, the lymph nodes and the lung, while the thyroid metastases are rare. We report on a 74-year-old man who consulted for a neck mass, dysphonia and dyspnea. The swelling of the thyroid gland was visible. The clinical examination revealed a large goiter (13/9cm), painful, fixed and suspect with a collateral circulation. The cervical ultrasound showed a basicervical mass and the cervical scintigraphy highlighted cold nodules. The thyroid fine needle aspiration revealed metastasis from a renal carcinoma. The cervical-thoracic CT scan showed a right renal tumor, a left laterocervical process and a mediastinal process with pulmonary nodules. A radical nephrectomy was performed and the histological exam showed a tubulo-papillary carcinoma. The targeted therapy was introduced with a clinical and radiological partial response. We highlight that a thyroid mass may be a metastasis which can be related to the evolution of a cancer that cannot be very frequent such as the kidney’s carcinoma. The fine needle aspiration of the thyroid nodules greatly helps the balance sheet in search of the primary tumor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".