An Evidence‐based Approach to Familial Nonmedullary Thyroid Cancer: Screening, Clinical Management, and Follow‐up
Bibliographic record
Abstract
UNLABELLED: Approximately 5% of nonmedullary thyroid cancers are of familial origin. When two or more family members are diagnosed with nonmedullary thyroid cancer in the absence of other known associated syndromes it is termed familial nonmedullary thyroid cancer (FNMTC). The genetic inheritance of FNMTC remains unknown, but it is believed to be an autosomal dominant mode of inheritance with incomplete penetrance and variable expressivity. FNMTC has been shown to be more aggressive and to have a worse prognosis than sporadic nonmedullary thyroid cancer. For example, studies have demonstrated that individuals with FNMTC have an increased risk of multifocal disease, local invasion, and lymph node metastases. These aggressive features appear to contribute to the higher recurrence rate and decreased disease-free survival seen in FNMTC patients compared to those with sporadic differentiated thyroid cancer. This article is an overview of the literature available in the English language discussing FNMTC. Critical questions regarding the screening, management, and follow-up of these patients are addressed with answers proposed based on the available literature. The quality of the evidence is ranked according to Sackett's criteria. Overall, the literature quality is somewhat limited, based on the low prevalence of FNMTC, the difficulty in identifying familial cases, the variable study designs, and limited long-term follow-up. CONCLUSIONS: To date, the optimal clinical approach is yet to be established, but improved awareness and screening will permit earlier detection, more timely intervention, and hopefully improved outcomes for patients and their families.
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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 | 0.042 | 0.141 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".