A Comparison of Patient-Reported Outcomes Following Consent for Genetic Testing Using an Oncologist- or Genetic Counselor-Mediated Model of Care
Bibliographic record
Abstract
This study compares knowledge, experience and understanding of genetic testing, and psychological outcomes among breast and ovarian cancer patients undergoing multi-gene panel testing via genetic counselor-mediated (GMT) or oncologist-mediated (OMT) testing models. A pragmatic, prospective survey of breast and ovarian cancer patients pursuing genetic testing between January 2017 and August 2019 was conducted at the Princess Margaret Cancer Centre in Toronto, Canada. A total of 120 (80 GMT; 40 OMT) individuals completed a survey administered one week following consent to genetic testing. Compared to OMT, the GMT cohort had higher median knowledge (8 vs. 9; p = 0.025) and experience/understanding scores (8.5 vs. 10; p < 0.001) at the time of genetic testing. Significant differences were noted in the potential psychological concerns experienced, with individuals in the GMT cohort more likely to screen positive in the hereditary predisposition domain of the Psychosocial Aspects of Hereditary Cancer tool (55% vs. 27.5%; p = 0.005), and individuals in the OMT cohort more likely to screen positive in the general emotions domain (65.0% vs. 38.8%; p = 0.007). The results of this study suggest that OMT can be implemented to streamline genetic testing; however, post-test genetic counseling should remain available to all individuals undergoing genetic testing, to ensure any psychologic concerns are addressed and that individuals have a clear understanding of relevant implications and limitations of their test results.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".