Current National Health Insurance Policies for Thyroid Cancer Prophylactic Surgery in the United States
Bibliographic record
Abstract
The efficacy of prophylactic thyroidectomy in patients with positive RET mutational analysis, familial thyroid cancer, or both has been reported. As cost has become critical to medical decision-making, this study was designed to evaluate currently existing coverage policies for prophylactic thyroidectomy. A confidential detailed cross-sectional nationwide survey of 481 medical directors from the American Association of Health Plans, Medicare, and Medicaid was conducted. Of the 150 respondents, 65% (n = 97) had 100,000 or more enrolled members, and 35% (n = 53) had fewer than 100,000 enrolled members. Only 9% of private plans have specific policies for coverage of prophylactic thyroidectomy for patients with a strong family history of thyroid cancer, 19% provided no coverage, and 72% had no policy. Only 9% of private plans have specific policies for patients with a known thyroid cancer genetic mutation, 12% provided no coverage, and 79% had no policy. Governmental carriers were less likely to provide coverage for prophylactic surgery: 4% for a strong family history and 6% for a genetic mutation. Altogether, 52% of government carriers provided no coverage for patients with a strong family history, and 50% provided no coverage in patients with a known genetic mutation; 44% of governmental carriers had no policy for either clinical scenario. Limited health insurance coverage for prophylactic thyroidectomy is offered in both private and governmental plans, with variations in coverage. As genetic testing becomes more widespread and with the potential identification of a gene predisposing to familial nonmedullary thyroid cancer, more uniform policies should be established to enable appropriate high risk candidates broader, equal coverage and access to these procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".