Correction: BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors
Bibliographic record
Abstract
Correction to: Genetics in Medicinehttps://doi.org/10.1038/s41436-018-0406-9; published online 15 January 2019 The original version of this article contained a typographical error in Eq. 2. The summation in the equation should run up to μ=6, and so Eq. 2 should have readλ(i)(t)=λ0(t)exp∑μ=16βMGμ(t)+∑ρβRFρμ(t)·zRFρ(i)∏ν=1μ-11-Gν(i)Gμ(i)+βPG(t)xP(i), where μ=6 corresponds to a noncarrier of pathogenic variants in the major genes, with βMG6(t)=0, and G6(i)=1 for noncarriers of pathogenic variants, and 0 otherwise. All presented results and conclusions were based on the correct version of the equation and are not influenced by this typographical error. This has now been corrected in both the PDF and HTML versions of the Article. The authors regret this error. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factorsGenetics in MedicineVol. 21Issue 8PreviewBreast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs). Full-Text PDF Open Access
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".