<i>BRCA</i> mutation testing for ovarian cancer in the context of available targeted therapy: Survey and consensus of Hong Kong specialists
Bibliographic record
Abstract
AIMS: BRCA mutation (BRCAmut) testing is an important tool for the risk assessment, prevention and early diagnosis of breast cancer (BC) and ovarian cancer (OC), and more recently, for determining patient susceptibility to targeted therapy. This study assessed the current BRCAmut testing patterns and explored physicians' perspectives on the utilities and optimal sequencing of the testing, in order to facilitate and standardize testing practices. METHODS: Medical specialists in BC and OC in Hong Kong were invited to complete a questionnaire on BRCAmut testing practices. A panel of specialists with extensive BRCAmut testing experience was also convened to develop consensus statements on testing, using the Delphi method and an anonymous electronic voting system. RESULTS: The survey respondents (n = 71) recognized family history (FH) of BC and/or OC and an early age of onset as key factors for referring BRCAmut testing. The proportion of respondents who would test all OCs regardless of FH or age, as per the recent international guideline, was low (28.2%). The largest hurdles to testing were the cost, as well as the availability of next-generation sequencing-accredited testing and genetic counseling facilities. The panelists suggested that the sequence of somatic testing followed by germline testing may help address both the imminent need of treatment planning and longer term hereditary implications. The potential emotional and financial burdens of BRCAmut testing should be weighed against the potential therapeutic benefits, and the type and timing of testing personalized. CONCLUSIONS: Accessibility of BRCAmut testing to all at-risk individuals will be achievable through improvements in testing affordability, as well as widened availability of accredited testing and genetic counseling facilities.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".