Contraception in breast cancer survivors from the FEERIC case-control study (performed on behalf of the Seintinelles research network)
Bibliographic record
Abstract
Abstract Objective To compare the prevalence of contraception in breast cancer (BC) patients at risk of unintentional pregnancy ( i . e . not currently pregnant or trying to get pregnant) and matched controls. Design The FEERIC study (Fertility, Pregnancy, Contraception after BC in France) is a prospective, multicenter case-control study. Data were collected through online questionnaires completed on the Seintinelles* research platform. Setting Not applicable Patient(s) BC patients aged 18-43 years, matched for age and parity to cancer-free volunteer controls in a 1:2 ratio. Intervention(s) None Results In a population of 1278 women at risk of unintentional pregnancy, the prevalence of contraception at study inclusion did not differ significantly between cases (340/431, 78.9%) and controls (666/847, 78.6%, p =0.97). However, the contraceptive methods used were significantly different, with a higher proportion of copper IUD use in BC survivors (59.5% versus 25.0% in controls p <0.001). For patients at risk of unintentional pregnancy, receiving information about chemotherapy-induced ovary damage at BC diagnosis (OR= 2.47 95%CI [1.39 - 4.37] and anti- HER2 treatment (OR=2.46, 95% CI [1.14 - 6.16]) were significantly associated with the use of a contraception in multivariate analysis. Discussion In this large French study, BC survivors had a prevalence of contraception use similar to that for matched controls, though almost one in five women at risk of unintentional pregnancy did not use contraception. Dedicated consultations at cancer care centers could further improve access to information and contraception counseling.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".