Interpretation of ovarian cancer risk and subsequent consideration of risk-reducing salpingo-oophorectomy in hereditary breast and ovarian cancer and Lynch syndrome.
Bibliographic record
Abstract
Hereditary breast and ovarian cancer and Lynch syndrome are hereditary conditions that increase risk of developing cancer, including epithelial ovarian cancer (EOC). The risk of developing EOC is dependent on the inherited gene with a pathogenic variant (e.g., BRCA1 or PMS2). Risk-reducing salpingo-oophorectomy (RRSO) is a procedure in which fallopian tubes and ovaries are removed to reduce the risk of EOC. In Manitoba, individuals at risk of gynecologic cancers may be referred to the Hereditary Gynecology Clinic (HGC) to discuss and/or complete RRSO. To better serve individuals at risk for EOC, we wished to understand factors that influence decisions to undergo RRSO across a spectrum of risk levels. This study is composed of two arms. Arm 1 is a retrospective chart review to characterize the overall study population stratified by risk level. Arm 2 is a mixed-methods study involving an online survey and interviews to understand how people interpret their risk of EOC and make decisions regarding RRSO. Arm 1 showed that referral to, as well as completion of RRSO at, the HGC is significantly associated with risk level. Additionally, there is high variability in referral to the HGC for the low-risk population. Age is a significant predictor of completion of RRSO at the HGC, with probability of completion peaking at ~52-years-old, then declining. Arm 2 indicates that although participants found objective risk information beneficial, integration of ‘experiential knowledge,’ and perceived control over each cancer type was important for subjective risk interpretation. Overall, factors such as childbearing, hormonal impacts, perceived risk and control were important to the decision-making process across all risk levels. Both direct and indirect influences of healthcare providers on the decision-making process were evident.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".