Levonorgestrel-releasing intrauterine system and the risk of breast cancer: A nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: Prolonged steroid hormone therapy increases the risk of breast cancer, especially the risk of lobular cancer, but the effect of the levonorgestrel-releasing intrauterine system (LNG-IUS) use is controversial. In this study we aimed to test the hypothesis that risk for lobular breast cancer is elevated among LNG-IUS users. MATERIAL AND METHODS: We identified from the national Medical Reimbursement Registry of Finland the women aged 30-49 who had used LNG-IUS for the treatment or prevention of menorrhagia in 1994-2007, and from the Finnish Cancer Registry breast cancers diagnosed before the age of 55 and by the end of 2012. RESULTS: A total of 2015 women had breast cancer diagnosed in a cohort of 93 843 LNG-IUS users during follow-up consisting of 1 032 767 women-years. The LNG-IUS users had an increased risk for both ductal breast cancer [standardized incidence ratio (SIR) 1.20, 95% confidence interval (CI) 1.14-1.25] and for lobular breast cancer (SIR 1.33, 95% CI 1.20-1.46), as compared with the general female population. The highest risk was found in LNG-IUS users who purchased the device at least twice, whose SIR for lobular cancer was 1.73 (95% CI 1.37-2.15). CONCLUSIONS: The results imply that intrauterine administration of levonorgestrel is not only related to an excess risk of lobular breast cancer but also, in contrary to previous assumptions, to an excess risk of ductal breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".