Risk of second primary malignancies following radioactive iodine therapy in thyroid cancer: A meta-analysis of 1,189,992 patients
Bibliographic record
Abstract
While the prognosis of thyroid cancer (TC) was generally excellent, there was a long-term concern regarding the risk of subsequent second primary malignancies (SPM) following radioactive iodine (RAI). This study aimed to comprehensively estimate the pooled risk of SPM occurrence following RAI therapy. The study searched the literature across three databases (PubMed, Scopus, and ScienceDirect), and was followed by citation searching. Original observational studies assessing the risk of SPM following RAI in TC were included. The Newcastle Ottawa scale was used for methodological assessment. STATA 17.0 was used for statistical analysis. A total of 18 studies encompassing 1,189,992 patients were included (638,275 in the RAI group and 551,717 in the non-RAI group). Pooled meta-analysis demonstrated no statistically significant increase in the risk of SPMs in the RAI-treated group, with a pooled RR of 1.07 (95% CI: 0.96 –1.21; p = 0.23). This result was deemed robust based on leave-one-out analysis. Subgroup analysis across publication year, follow-up duration (>10 years vs ≤10 years), population size, and SPM site (including breast, genitourinary, gastrointestinal-hepatobiliary, respiratory, skin, hematology, central nervous system, head and neck) consistently showed comparable results. There was no evidence of a significantly increased risk of SPM among TC survivors treated with RAI.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.054 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".