Deciphering the Risk of Developing Second Primary Thyroid Cancer Following a Primary Malignancy—Who Is at the Greatest Risk?
Bibliographic record
Abstract
Background: It is critical to understand factors that may contribute to an increased risk of SPTC in order to develop surveillance protocols in high-risk individuals. This systematic review and meta-analysis will assess the association between primary malignancy and SPTC. Methods: A search of PubMed and Embase databases was completed in April 2020. Inclusion criteria included studies that reported the incidence or standardized incidence ratio of any primary malignancy and SPTC, published between 1980–2020. The PRISMA guidelines were followed and the Newcastle–Ottawa Scale was used to assess quality of studies. Results: 40 studies were included, which were comprised of 1,613,945 patients and 15 distinct types of primary cancers. In addition, 4196 (0.26%) patients developed SPTC following a mean duration of 8.07 ± 4.39 years. Greater risk of developing SPTC was found following primary breast (56.6%, 95%CI, 44.3–68.9, p < 0.001), renal cell (12.2%, 95%CI, 7.68–16.8, p < 0.001), basal cell (7.79%, 95%CI, 1.79–13.7, p = 0.011), and ovarian cancer (11.4%, 95%CI, 3.4–19.5, p = 0.005). SPTC patients were more likely to be females (RR = 1.58, 95%CI, 1.2–2.01, p < 0.001) and Caucasians (p < 0.001). Conclusions: Surveillance protocols should be considered for patients at a higher risk of SPTC, including those with primary breast, renal cell, basal cell and ovarian cancers who are female and/or Caucasian.
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 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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".