Comprehensive analysis of the Ontario eligibility criteria for Hereditary Breast and Ovarian Cancer Syndrome
Bibliographic record
Abstract
Germline mutations in breast and ovarian cancer are rare, with approximately 10% and 13% being hereditary in origin, respectively. In 2001, the Ontario Ministry of Health and Long Term Care (MOHLTC), in an effort to contain costs, defined criteria to determine an individual’s eligibility for BRCA genetic screening. Using real-world data from Kingston General Hospital (KGH) and Mt. Sinai Hospital (Toronto), we assessed if the long-standing program that has been in use in Ontario for nearly two decades provides real-world evidence for their effectiveness. From our studies we have produced the following recommendations to expand and/or improve the existing eligibility guidelines: 1. Clinical geneticists and genetic counselors should adhere strictly to risk criteria guidelines as this appears to be an economical approach that should not reduce the effectiveness of screening; 2. Add an additional criterion to include probands who do not themselves have ovarian cancer but who have first degree relatives with ovarian cancer, may be useful. 3. Change the risk criteria #10 requirements to include an age cut-off that maximizes the capture rate of carriers while reducing noncarriers. Our data show this value to potentially be 60 years old; 4. Risk assessment tools should be applied rigorously and consistently for all patients. Standardizing these practices ensure a normalized approach for all patients; 5. Do not test individuals that fail to meet eligibility criteria nor risk assessment tool risk thresholds; 6. Objectively describing what constitutes “clinical suspicion” RC#13 may help standardize how this risk criteria is assigned across institutions. 7. Information collection and exchange of patient data within the context of BRCA1/2 screening should be standardized across all institutions in Ontario.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".