Specialty Care and Counselling about Hereditary Cancer Risk Improves Adherence to Cancer Screening and Prevention in Newfoundland and Labrador Patients with <em>BRCA1/2</em> Pathogenic Variants: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
BRCA1 and 2 pathogenic variants increase lifetime risks of breast (50-75%) and ovarian cancer (15-20%). Guidelines recommend breast screening (MRI and mammogram) or risk-reducing mastectomy with salpingo-oophorectomy for ovarian cancer prevention. We sought to 1) characterize the population of BRCA1/2 carriers in Newfoundland and Labrador (NL) 2) evaluate risk-reducing interventions 3) identify factors influencing screening and prevention adherence. Methods: This is a retrospective study from a population-based provincial cohort of BRCA1/2 carriers. Eligibility criteria for risk-reducing interventions were defined for each case and patients were categorized based on their level of adherence with recommended screening or prevention. Chi-squared and regression analyses were used to determine which factors influenced uptake and level of adherence. Results: 276 BRCA1/2 carriers were identified; 156 living NL females composed the study population. Unaffected females were younger at testing than those with a cancer diagnosis (44.4 y versus 51.7 y; p=0.002). Categorized by eligibility, 61.0%, 61.6%, 39.0% and 75.7% of patients underwent MRI, mammogram, risk-reducing mastectomy, and risk-reducing salpingo-oophorectomy, respectively. Individuals with breast cancer were more likely to have risk-reducing mastectomy (64.7% versus 35.3%; p<0.001), and those who attended specialty hereditary cancer clinic were more likely to be adherent with breast/ovarian cancer recommendations (73.2% versus 13.4%; p<0.001) and to undergo risk-reducing salpingo-oophorectomy(84.1% versus 15.9%; p<0.001). Conclusions: Nearly 40% of female BRCA1/2 carriers were not receiving breast surveillance according to evidence-based recommendations. Cancer risk reduction and uptake of breast imaging and prophylactic breast and pelvic surgeries are significantly higher in patients who receive dedicated specialty care. Organized hereditary cancer prevention programs will be a valuable component of Canadian health care systems and have the potential to reduce the burden of disease country-wide.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".