Online Screening and Virtual Patient Education for Hereditary Cancer Risk Assessment and Testing
Bibliographic record
Abstract
OBJECTIVE: To use online screening and virtual patient education tools to improve the provision of hereditary cancer risk assessment. METHODS: We conducted a prospective, single-arm study in which clinicians at five U.S. community obstetrics and gynecology practices underwent an 8-week observation followed by 3-4 weeks of training on online patient screening and virtual patient education (prerecorded video with or without a genetic counselor phone call) for genetic testing-eligible patients. After a 4-week practice period, hereditary cancer risk assessment and patient education metrics were collected at 8 weeks and compared with preintervention metrics using univariate conditional logistic regression models stratified by site. The primary outcome was the change in genetic testing completion rate. Clinicians and patients were invited to complete a satisfaction survey. RESULTS: A total of 5,795 and 5,135 patients were seen before and after the intervention, respectively. The proportion of screened patients meeting testing guidelines increased from 21.6% before the intervention to 28.2% after the intervention (odds ratio [OR] 1.36, 95% CI, 1.26-1.47, P <.001). Guideline-eligible patients were significantly more likely to be offered genetic testing (59.1% vs 89.1%, OR 2.06, 95% CI, 1.87-2.27, P <.001), to submit a sample (32.9% vs 45.0%, OR 1.49, 95% CI, 1.27-1.74, P <.001), and to complete testing (16.0% vs 34.2%, OR 2.38, 95% CI, 2.00-2.83, P <.001). Most clinicians agreed or strongly agreed that the screening tool improved the identification of patients meeting hereditary cancer risk assessment guidelines (92.1%), saved time (64.9%), and was easy to incorporate (68.4%) and that patient education improved their ability to deliver hereditary cancer risk assessment standard of care (84.2%). Most patients agreed or strongly agreed that virtual education helped them understand the purpose (91.7%) and implications (92.6%) of genetic testing. CONCLUSION: A guideline-based online patient screening tool and virtual patient education were well received. The online tool enabled identification of significantly more guideline-eligible candidates for hereditary cancer risk assessment, and education improved patients' genetic literacy. Together, these tools ultimately improved the genetic testing completion rate.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".