Genetic Testing for All: Overcoming Disparities in Ovarian Cancer Genetic Testing
Bibliographic record
Abstract
Nearly 3% of the population carries genetic variants that lead to conditions that include hereditary breast and ovarian cancer and Lynch syndrome. These pathogenic variants account for approximately 20% of ovarian cancer cases, and those with germline pathogenic variants have an odds ratio between 4 and 40 for developing ovarian cancer compared with noncarriers. Given the high prevalence of genetic variants, multiple organizations, including ASCO, recommend universal genetic counseling and testing for women diagnosed with epithelial ovarian cancer. Unfortunately, most individuals with a hereditary ovarian cancer syndrome are unaware of their underlying mutation, and racial and ethnic minority individuals as well as patients of low socioeconomic status experience disproportionate rates of underrecognition, leading to late and missed diagnoses. In this article, we review the current understanding of disparities in genetic testing for people with ovarian cancer, the role of population-based genetic testing, and innovative strategies to overcome the critical inequities present in current cancer genetic medicine. Underuse and disparities related to accessing recommended genetic services are complex and multifactorial, requiring improvements in processes related to provider identification, genetic counseling and testing referral, and patient uptake/adherence. Through the expansion of remote genetic counseling, offering online strategies for genetic testing, and reaching at-risk relatives through direct relative contact cascade testing and population-based genetic testing, there are a growing number of innovations in the field of genetic medicine, many of which emphasize health equity and offer promising alternatives to the current paradigm of genetic testing.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".