Additional file 1 of Risk factors for breast cancer development by tumor characteristics among women with benign breast disease
Bibliographic record
Abstract
Additional file 1: Supplemental methods. Supplementary Figure 1. Steps of multiple imputation (for more details see Appendix A). Supplementary Table 1. Characteristics of BBD histology and breast cancers among cases by calendar year of BBD and breast cancer diagnoses (N = 514). Supplemental Table 2. Comparison of associations between select patient characteristics and histologic features with breast cancer risk in conditional and unconditional logistic regression models using multiple imputation dataset (N = 1028). Supplemental Table 3. Associations between select patient characteristics and histologic features with breast cancer risk by tumor grade (N = 922). Supplemental Table 4. Associations between select patient characteristics and histologic features with breast cancer risk by tumor size (N = 1013). Supplementary Table 5. Associations between demographic and histologic features with breast cancer risk by BBD calendar year before vs. after 1993 (N = 1028). Appendix A. Distribution of numeric variables before (top) and after (bottom) multiple imputations.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.830 | 0.141 |
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".