The Effectiveness of Family History Questionnaires in Cancer Genetic Counseling
Bibliographic record
Abstract
The number of individuals receiving genetic counseling for hereditary breast and ovarian cancer syndrome has steadily risen. To triage patients for genetic counseling and to help reduce the amount of time needed by a genetic counselor in direct patient contact, many clinics have implemented the use of family history questionnaires. Although such questionnaires are widely used, scant literature exists evaluating their effectiveness. This article explores the extent to which family history questionnaires are being used in Ontario and addresses the utility of such questionnaires in one familial cancer clinic. By comparing the pedigrees created from questionnaires to those updated during genetic counseling, the accuracy and effectiveness of the questionnaires was explored. Of 121 families recruited into the study, 12% acquired changes to their pedigree that led to a revised probability estimate for having a BRCA1 or BRCA2 mutation and 5% acquired changes that altered their eligibility for genetic testing. No statistically significant difference existed between the eligibility for genetic testing prior to and post counseling. This suggests that family history questionnaires can be effective at obtaining a family history and accurately assessing eligibility for genetic testing. Based on the variables that were significantly associated with a change in probability estimate, we further present recommendations for improving the clarity of such questionnaires and therefore the ease of use by patients.
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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 | 0.100 | 0.269 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".