Abstracts for the 41st Human Genetics Society of Australasia Annual Scientific Meeting Brisbane, Queensland August 5–8, 2017
Bibliographic record
Abstract
Germline mutations in cancer predisposition genes are increasingly being used to inform breast cancer treatment, resulting in an increased number of Familial Cancer Centre referrals for treatmentfocused genetic testing (TFGT). TFGT requires urgency not typical in genetic testing for familial cancers. This urgency presents psychosocial challenges for our clients and, as genetic counselors, the necessity to increase our understanding of chemotherapy options and surgical interventions. A collaboration between genetic counselors and oncologists at the Parkville Familial Cancer Centre (PFCC) led to the development of an annotated clinical pathway, outlining typical breast cancer treatment options and the key time points where genetic testing may have an impact on treatment. Through the use of case studies, we highlight genetic counseling challenges faced as a result of TFGT, as well as psychosocial and clinical impacts specific to TFGT. The development of an annotated clinical pathway has informed our intake process for TFGT referrals. We are now better able to understand the possible psychosocial impacts of genetic testing for our patients undergoing TFGT and have an improved understanding of the common medical treatment pathways for breast cancer. Furthermore, the psychosocial challenges that arise during TFGT are important considerations as mainstreaming programs move this type of testing away from the genetics clinic and into an oncological setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".