MG-103 Determining genetics referral eligibility for hereditary breast/ovarian cancer risk assessment: An electronic solution
Bibliographic record
Abstract
Objectives To develop and validate an electronic tool to enhance referrals to the Familial Breast/Ovarian Cancer Clinic. Design/methods Patients attending the Breast Diagnostic Clinic were recruited for this non-randomised 3-phase study, where paper questionnaires (PQ) and surgeon assessments were traditionally used to determine Genetics referral eligibility. Phase 1: Patients completed PQ (N = 201). Phase 2: Electronic tool (ET) developed; tested for usability, readability, design, interface. Tool accuracy assessed by comparing results for patients completing both PQ and ET (N = 100). Phase 3: Patients completed ET only (N = 200). Health records reviewed to determine data accuracy. Number of study patients eligible for referral compared with referrals received across all 3 phases. Patient/provider satisfaction assessed. Results Gender, education, number of patients with breast/ovarian cancer, age at diagnosis, family history, proportion of patients meeting referral criteria were similar across all study phases. No statistically significant difference in either number of patients eligible for referral or overall referral rates (Phase 1: 16.9%; Phase 3: 18.0%) was found. 67% of patients preferred ET over PQ. Patients found ET questions easier to complete and understand. Physician satisfaction was higher with ET in reviewing family history, identifying eligible patients, making timely referrals. Conclusions The electronic tool was accurate and useful in determining referral eligibility. Similar referral rates were seen across study phases. Patients and physicians had positive experiences with the ET. It is feasible to use this tool to identify patients eligible for Genetics referral. Further studies are in progress to investigate why some eligible patients were not referred.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".