Women and thyroid cancer incidence: overdiagnosis versus biological risk
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Our aim is to discuss the concepts of sex and gender in the context of thyroid cancer epidemiology. RECENT FINDINGS: It has been long-established in global epidemiologic data that thyroid cancer incidence rates are higher in women than men. However, what has been less well understood is whether this reflects sex disparities in cancer susceptibility, gender disparities in detection, or a combination. A recent meta-analysis of autopsy data from individuals who were not known to have thyroid cancer in their lifetime demonstrated no difference in the prevalence of thyroid cancer in women and men, suggesting that gender differences may be the reason for gender-based differences in thyroid cancer detection. This finding, and sex differences in auto immunity and other factors that may affect cancer susceptibility are explored. SUMMARY: Additional research to explore gender- and sex-specific data on thyroid cancer would inform our understanding of the differences and similarities between men and women in susceptibility and detection of thyroid cancer and help to optimize disease management for all genders and both sexes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".