Additional file 3 of Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment
Bibliographic record
Abstract
Additional file 3: Supplementary Table S8, Supplementary Table S9, Supplementary Table S10, Supplementary Table S11, and Supplementary Table S12. Supplementary Table S8. Shows the BFDPs under two more restrictive prior probabilities of true association (10-5 and 10-6) for the results presented in Table 1. Supplementary Table S9. Shows the BFDPs under two more restrictive prior probabilities of true association (10-5 and 10-6) for the results presented in Table 2. Supplementary Table S10. Shows power calculation by subgroup, at the two-sided 5E-08 level for varying genotype hazard ratio (GHR) and minor allele frequency (MAF), based on number of cases and event rate from the 15-year breast cancer-specific analyses. Supplementary Table S11. Shows power calculation by subgroup, at the two-sided 5E-08 level for varying genotype hazard ratio (GHR) and minor allele frequency (MAF), based on number of cases and event rate from the 5-year breast cancer-specific analyses. Supplementary Table S12. Shows the subgroup-specific associations detected by previous studies and corresponding estimates from the current study.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.504 | 0.069 |
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".