Do traditional Gail model risk factors account for increased breast cancer in women with lupus?
Bibliographic record
Abstract
OBJECTIVE: To determine to what extent the observed experience of breast cancer in a combined cohort of patients with systemic lupus erythematosus (SLE) could be explained by the profile of breast cancer risk factors. METHODS: Data were pooled from 2 centers, the Montreal General Hospital and the Feinberg School of Medicine at Northwestern University in Chicago. For each female cohort member, the probability of developing breast cancer during followup was estimated based on factors (including the individual's age, parity, age at first live birth, age of menarche, personal history of benign breast disease, and family history) using the Gail model, an established model for predicting breast cancer risk. The actual occurrence of cancer cases was determined by linkage with regional cancer registries. RESULTS: Of the 583 women in the combined cohort, 5 had been diagnosed with breast cancer prior to cohort entry, and 14 declined participation. In those remaining, 12 cases of breast cancer occurred compared to 5.6 predicted by the Gail model (standardized incidence ratio 2.1, 95% confidence interval: 1.1, 3.7). Thus, after controlling for risk factors, the incidence of breast cancer was elevated. CONCLUSION: Our data suggest that the risk of breast cancer in our SLE cohort is not completely explained by traditional factors found in the Gail model. Other factors, such as carcinogenic exposures (i.e., alkylating agents and immunosuppressive drugs) or the immunologic dysregulation of SLE itself, may be contributory.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".