Bibliographic record
Abstract
Polygenic tests such as genome-wide small nucleotide polymorphism (SNP) risk testing, exome or genome sequencing are currently on the horizon for genetic testing for inherited cancer risk. We are unsure of how patients would accept this future genetic testing and the best way to fully understand the experience of undergoing a polygenic test for breast cancer risk is to explore the experience of women who have already undergone the process. In Ontario, these individuals are those who have already had gene panel testing (GPT). This group’s opinions and experiences will be directly related to the refinement and modification of the existing GPT process and will provide guidance for polygenic testing offered in the future. Methods: Fourteen women who have undergone GPT in the past year were interviewed in a semi-structured manner regarding their GPT experience. Interviews were recorded and transcribed, then qualitatively coded to identify key words, phrases, and expressed concepts surrounding GPT. Results: Participants had an overall favourable opinion regarding their GPT experience, with minor changes to be considered in future provision of GPT. In general, GPT appears to be well-tolerated within the context of a traditional genetics assessment and participants that did not receive a clinically significant result through GPT felt that they would be open to pursuing other forms of genetic testing in the future such as polygenic testing, despite the possibility of receiving an uncertain result.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".