Familial Non‐Medullary Thyroid Cancer: A Matched‐Case Control Study
Bibliographic record
Abstract
OBJECTIVES: Familial non-medullary thyroid cancer (FNMTC) is a newly recognized disease entity and can be distinguished from the more common sporadic non-medullary thyroid cancer. The purpose of this study was to determine some of the potential distinguishing features of FNMTC. STUDY DESIGN: Retrospective association study and matched-case control study. METHODS: Five hundred forty-three cases of well-differentiated follicular origin thyroid cancers were identified and collected in a database. Among this population, 24 cases of FNMTC were identified. A case of FNMTC was defined as a patient with the following two criteria: a well-differentiated follicular origin thyroid cancer and at least one first-degree relative with a well-differentiated epithelial origin thyroid cancer. The unmatched sporadic and FNMTC groups were compared using t test, Phi test, Cramer V test, and Pearson and Spearman correlation tests. Twenty-four FNMTC cases were matched to 24 sporadic cases based on age, gender, stage of disease at presentation, and tumor size. Clinicopathologic features, management, and outcome were analyzed statistically using a matched-proportional z test. Disease-free survival and disease-specific survival were analyzed using log-rank test and the Kaplan-Meier function. A P-value less than .05 was considered statistically significant. RESULTS: : There was no significant difference in ionizing radiation exposure, disease multifocality, surgical management, or recurrence between the sporadic and FNMTC patients. Although FNMTC patients tend to have improved disease-free survival and disease-specific survival, the difference was not significant at the 5% level. CONCLUSION: Although FNMTC is characterized by strong family history, these patients do not tend to have worse prognosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".