Preeclampsia and risk of breast cancer: A longitudinal cohort study of tumor histology
Bibliographic record
Abstract
Patients with preeclampsia have a reduced risk of breast cancer, but it is not clear if the protective effect extends to all types of breast tumors. Our objective was to determine the association of preeclampsia with ductal, lobular, and other breast cancer histology. We conducted a longitudinal cohort study of 1,459,716 patients who had pregnancies between 1989 and 2022 in Quebec, Canada. The main exposure measure was preeclampsia. The outcome was breast cancer, including ductal, lobular, and other histological subtypes diagnosed up to 34 years after childbirth. We included in situ, localized invasive, and metastatic breast cancer. We used Cox regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between preeclampsia and breast cancer histology, adjusted for maternal characteristics. Patients with preeclampsia had a lower incidence of breast cancer than patients without preeclampsia (82.1 vs. 111.7 per 100,000 person-years). Preeclampsia was associated with a 16% lower risk of breast cancer compared with no preeclampsia (HR 0.84, 95% CI 0.79-0.89), including a 14% lower risk of ductal (HR 0.86, 95% CI 0.81-0.93) and 31% lower risk of lobular tumors (HR 0.69, 95% CI 0.55-0.87). The protective association was present for in situ, localized invasive, and metastatic breast tumors. Preeclampsia was not associated with mucinous, medullary, papillary, or other breast cancer histology. We conclude that patients with preeclampsia are less likely to develop ductal and lobular breast cancer than patients with normotensive pregnancies, but do not have a reduced risk of other types of breast cancer.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".