Log odds of carrying an Ancestral Mutation in BRCA1 or BRCA2for a Defined personal and family history in an Ashkenazi Jewish woman (LAMBDA)
Bibliographic record
Abstract
INTRODUCTION: Ancestral mutations in BRCA1 and BRCA2 are common in people of Ashkenazi Jewish descent and are associated with a substantially increased risk of breast and ovarian cancer. Women considering mutation testing usually have several personal and family cancer characteristics, so predicting mutation status from one factor alone could be misleading. The aim of this study was to develop a simple algorithm to estimate the probability that an Ashkenazi Jewish woman carries an ancestral mutation, based on multiple predictive factors. METHODS: We studied Ashkenazi Jewish women with a personal or family history of breast or ovarian cancer and living in Melbourne or Sydney, Australia, or with a previous diagnosis of breast or ovarian cancer and living in the UK. DNA samples were tested for the germline mutations 185delAG and 5382insC in BRCA1, and 6174delT in BRCA2. Logistic regression was used to identify, and to estimate the predictive strength of, major determinants. RESULTS: A mutation was detected in 64 of 424 women. An algorithm was developed by combining our findings with those from similar analyses of a large study of unaffected Jewish women in Washington. Starting with a baseline score, a multiple of 0.5 (based on the logistic regression estimates) is added for each predictive feature. The sum is the estimated log odds ratio that a woman is a carrier, and is converted to a probability by using a table. There was good internal consistency. CONCLUSIONS: This simple algorithm might be useful in the clinical and genetic counselling setting. Comparison and validation in other settings should be sought.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".