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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".