The Use of Family History Questionnaires: An Examination of Genetic Risk Estimates and Genetic Testing Eligibility in the Non‐responder Population
Bibliographic record
Abstract
The use of mailed family history questionnaires (FHQs) has previously been established to be an effective method for obtaining family history information for the triage of patients for genetic counseling and genetic testing of hereditary breast and ovarian cancer syndrome; yet only 53% of patients complete their FHQ within 6 months from the date of mailing (Armel et al. Journal of Genetic Counseling, 18(4):366-378, 2009). Although literature exists evaluating why women may not attend genetic counseling, no data are currently available examining genetic risk or genetic testing eligibility in the population of patients not returning their FHQ (non-responders). Concern exists that if non-responders are not followed-up for the purpose of triage for genetic counseling, individuals at high-risk for a hereditary cancer syndrome may be missed. This article explores the demographics of the non-responder population to assess genetic risk estimates for mutations in the BRCA1 and BRCA2 genes and genetic testing eligibility as compared to a responder population of patients who completed a mailed FHQ. A total of 430 pedigrees were obtained, 215 from non-responders and 215 from responders. Results of this study indicate that 69% of non-responders were either unreachable by telephone (42%), declined an appointment (19%), or were previously seen in another center for a genetic counseling visit (8%). Additionally, results indicate that non-responders are less likely to be eligible for genetic testing (40%) as compared to responders (57%) (p = 0.0004). Together these data shed light on a population of patients for which limited information exists and suggest that we question how and to what extent clinics should pursue non-responders, particularly in light of global reductions in health care funding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".