Bibliographic record
Abstract
After the cloning of the second breast cancer gene in 1995 (BRCA2), gene researchers took divergent paths. Some searched for new and rare genes while others sought to explain unusual cancer clusters, but at Cambridge University, a group of genetic epidemiologists devoted their energies to the development of a polygenic model for breast cancer ( 1 ). They sought to account for the missing heritability through a model that postulated that there were many genes, each of which contributed in a small way to a woman’s vulnerability. The effort was facilitated through the completion of a genomic map of nucleotide variants (HapMap) and the engineering of a DNA chip that secured many thousands of single nucleotide polymorphisms (SNPs) from across the genome. The first genome-wide association study (GWAS) for breast cancer was published by the Cambridge group in 2007 ( 2 ), and there have been many others since. Doug Easton and his Cambridge colleagues engendered an extraordinary collaborative spirit, which culminates in a landmark paper boasting 218 authors ( 3 ) published in this issue of the Journal. The fruits of the enterprise are presented as a genetic risk assessment model that incorporates 77 SNPs. The authors use the model to generate personal risk scores and thereby stratify women according to their lifetime risk. The paper by Mavvadat et al. is important, because it enables us to evaluate the clinical utility of the polygene paradigm—it is unlikely that the model will be improved substantially if we add more SNPs (or different SNPs). For the 77 SNPs, the frequency of the rarer of the two alleles ranged from 0.001% to 48.2% and the corresponding odds ratios range from 0.86 to 1.36. The baseline risk of breast cancer to age 74 years in the UK is 8.2% ( 4 ). Based on her personal risk score, a woman in the top one percentile had a three-fold increase in risk relative to the mean (hazard ratio [HR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83), or roughly 25% lifetime.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".