Bibliographic record
Abstract
OBJECTIVE: To design a new management algorithm for all Hürthle cell tumors and variants based on histopathologic findings and the ret/PTC molecular marker. DESIGN: A retrospective medical record review. SETTING: A large tertiary care teaching center. PATIENTS: Forty-five consecutive cases of Hürthle cell carcinoma were gathered from a database of 661 patients with well-differentiated epithelial thyroid cancers compiled over 22 years. Data collected included patient information, tumor information, and treatment factors. MAIN OUTCOME MEASURES: Outcome parameters included tumor and treatment variables, which were analyzed statistically using the chi(2) and t tests. Disease-free survival and disease-specific survival analyses were performed using Kaplan-Meier analysis. RESULTS: A female-male ratio of 3:1 was found, with a median patient age of 57 years. Twenty-three patients had American Joint Commission on Cancer stage II disease. Treatment factors had no significant effect on disease recurrence or survival. More than half of the patients had histologically proved regional metastases. Vascular invasion significantly diminished disease-specific survival and disease-free interval. CONCLUSIONS: We found a high incidence of Hürthle cell carcinoma with cervical metastasis. On the basis of findings of this study and our previous clinical and molecular findings, we propose a treatment algorithm that combines histologic examination and molecular assays for the ret/PTC gene rearrangements specific to papillary thyroid carcinoma. After permanent section analysis demonstrating Hürthle cell metaplasia, the algorithm mandates completion thyroidectomy in patients with ret/PTC-positive Hürthle cell tumors and clinical observation for ret/PTC-negative Hürthle cell adenomas. We recommend more aggressive treatment of ret/PTC-positive Hürthle cell lesions (or Hürthle cell papillary thyroid cancer), because of the higher incidence of regional metastatic disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".