Thyroid cancer and multiple primary tumors in the SEER cancer registries1
Bibliographic record
Abstract
Thyroid cancer incidence rates have increased steadily in the United States and elsewhere. Radiation exposure at a young age is a strong risk factor, but otherwise the etiology is unclear. To explore etiologic clues, we studied the risk of thyroid cancer after an earlier primary cancer, as well as the risk of developing multiple primaries after an earlier thyroid cancer in the U.S. Surveillance, Epidemiology and End-Results (SEER) cancer registries program (1973-2000). In 2,036,597 patients diagnosed with any invasive cancer who survived for a minimum of 2 months, we observed a 42% increased risk compared to the general population for second thyroid cancer based on 1,366 cases (95% confidence interval (CI) = 35-50%; excess absolute risk (EAR) = 0.38/10,000 person-years (PY)). Elevated risks were observed after most cancer sites studied. The most pronounced excess (observed/expected (O/E) = 2.86) was seen for second thyroid cancers detected in the year after diagnosis of the first cancer. Among 29,456 2-month thyroid cancer survivors, 2,214 second cancers occurred (O/E = 1.11, 95% CI = 1.06-1.15; EAR = 7.64/10,000 PY). Again, the highest risk was seen in the first year (O/E = 1.26). Patients <40 years of age at diagnosis of thyroid cancer had a 39% increased risk of a second cancer, whereas for older patients the risk was elevated 6%. We observed consistently increased risks for cancers of the breast, prostate, and kidney, and a likely radiation treatment-related excess of leukemia. Based on small numbers of cases, cancers of the salivary glands, trachea, scrotum, adrenal glands, and brain and central nervous system (CNS) also occurred in excess. A decreased risk was observed for smoking-related malignancies. Thyroid cancer is associated with primary cancers of many different organs. Although enhanced medical surveillance likely plays a role, 2-way, positive associations between thyroid cancer and cancers of the breast, prostate, kidney, salivary glands, brain and CNS, scrotum, and leukemia suggest etiologic similarities and possible treatment effects.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".