Thyroid dysfunction and cancer incidence: a systematic review and meta-analysis
Bibliographic record
Abstract
In this study, we aimed to evaluate site-specific cancer risks associated with hyperthyroidism or hypothyroidism. We performed a systematic review of observational studies reporting associations between hyperthyroidism or hypothyroidism and subsequent site-specific cancer incidence, in MEDLINE and the COCHRANE library (inception-28/01/2019) (PROSPERO: CRD42019125094). We excluded studies with thyroid dysfunction evaluated as a cancer biomarker or after prior cancer diagnosis and those considering transient thyroid dysfunction during pregnancy or severe illnesses. Risk of bias was assessed using a modified Newcastle-Ottawa scale. Risk estimates were pooled using random-effects models when ≥5 studies reported data for a specific cancer site. Twenty studies were included, of which 15 contributed to the meta-analysis. Compared to euthyroidism, hyperthyroidism was associated with higher risks of thyroid (pooled risk ratio: 4.49, 95%CI: 2.84-7.12), breast (pooled risk ratio: 1.20, 95%CI: 1.04-1.38), and prostate (pooled risk ratio: 1.35, 95%CI: 1.05-1.74), but not respiratory tract (pooled risk ratio: 1.06, 95%CI: 0.80-1.42) cancers. Hypothyroidism was associated with a higher risk of thyroid cancer within the first 10 years of follow-up only (pooled risk ratio: 3.31, 95%CI: 1.20-9.13). There was no or limited evidence of thyroid dysfunction-related risks of other cancer sites. In conclusion, thyroid dysfunction was associated with increased risks of thyroid, breast, and prostate cancers. However, it remains unclear whether these findings represent causal relationships because information on treatments and potential confounders was frequently lacking.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".