Nonovarian Pelvic Cancers in <i>BRCA1/2</i> Mutation Carriers and the BRCAPRO Statistical Model
Bibliographic record
Abstract
Article Tools SPECIAL DEPARTMENTS Article Tools OPTIONS & TOOLS Export Citation Track Citation Add To Favorites Rights & Permissions COMPANION ARTICLES No companion articles ARTICLE CITATION DOI: 10.1200/JCO.2002.02.130 Journal of Clinical Oncology - published online before print September 21, 2016 PMID: 12228218 Nonovarian Pelvic Cancers in BRCA1/2 Mutation Carriers and the BRCAPRO Statistical Model Carol CreminxCarol CreminSearch for articles by this author , Nora WongxNora WongSearch for articles by this author , Karen BuzagloxKaren BuzagloSearch for articles by this author , Ann-Josée ParadisxAnn-Josée ParadisSearch for articles by this author , William FoulkesxWilliam FoulkesSearch for articles by this author D.A. BerryxD.A. BerrySearch for articles by this author , G. ParmigianixG. ParmigianiSearch for articles by this author , W. RubinsteinxW. RubinsteinSearch for articles by this author , P. WatsonxP. WatsonSearch for articles by this author Show More Sir M.B. Davis-Jewish General Hospital, McGill University, Montreal, Quebec, CanadaUniversity of Texas M.D. Anderson Cancer Center, Houston, TX https://doi.org/10.1200/JCO.2002.02.130 First Page Full Text PDF Figures and Tables © 2002 by American Society of Clinical OncologyjcoJ Clin OncolJournal of Clinical OncologyJCO0732-183X1527-7755American Society of Clinical OncologyResponse15092002In Reply:Cremin et al make an important point. BRCAPRO does not incorporate whether each family member has been diagnosed with fallopian tube or peritoneal cancer. The same is true for other cancers that may have different penetrance in mutation carriers than in noncarriers. BRCAPRO could be modified to incorporate family history information about these two cancers, but such a modification requires quantitative information about penetrance. This quantification does not exist. Until it becomes available and BRCAPRO can be modified, some users incorporate fallopian tube and peritoneal cancers as though they were ovarian cancers. This is at best an approximation, but it probably gives a more accurate assessment of risk than does ignoring them.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".