RESPONSE: Re: Oral Contraceptives and the Risk of Breast Cancer in BRCA1 and BRCA2 Mutation Carriers
Bibliographic record
Abstract
Hopper and Baron point out that there was no apparent difference in the smoking histories of the breast cancer case patients and matched control subjects in our recent case–control study of breast cancer among BRCA mutations carriers (1). They ask whether this observation is consistent with our data from an earlier report of the same study population, in which we proposed that cigarette smoking protected against breast cancer in BRCA mutation carriers (2). Hopper and Baron are correct. We have re-addressed the question of smoking and breast cancer in a much larger sample of BRCA mutation carriers and now find no support for the presence of a reduced risk (3). In our analysis, smoking was reported by 41.2% of 1097 case patients and by 40.4% of matched control subjects (3). The methods of this and our earlier study (2) are almost identical, and the different results are likely due to a difference in sample size. In the rush to publish in a competitive area, it is often the case that epidemiologic studies with marginal sample sizes, but that show interesting preliminary results, are the first to reach print. The report from Leiden by de Bock et al. might be another example of this phenomenon.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.024 | 0.015 |
| Insufficient payload (model declined to judge) | 0.039 | 0.023 |
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".