Utility of eConsult to enhance delivery of cancer genetic services and identify hereditary cancer knowledge gaps in primary care
Bibliographic record
Abstract
Purpose: This study analyzed the utility of electronic consultation (eConsult) for hereditary cancer (HC) and aimed to identify primary care practitioner (PCP) knowledge gaps. Methods: A retrospective mixed-methods study was used to evaluate 200 randomly selected PCP eConsult cases submitted to cancer genetics specialists in Ontario, Canada. Results: In 65% (129/200) of eConsults, PCPs indicated they received clear advice for a new course of action. In 34% (68/200), referral was contemplated but now avoided. In 8% (16/200), referral was advised when not originally planned. For 89% (177/200), eConsult was considered valuable. For most, (63%, 125/200), PCPs agreed eConsult addressed a clinical problem that should be incorporated into continuing medical education. PCPs' questions were mainly about cancer screening (114), genetic testing (107), or genetics referral (76). Geneticist recommendations were mainly about cancer screening (154), genetics referral (104), and the High-Risk Ontario Breast Cancer Screening Program (41). PCP knowledge gaps identified included cancer screening guidelines (112), genetics referral criteria (100), High-Risk Ontario Breast Cancer Screening Program screening criteria (71), and understanding of genetics principles (237). Conclusion: eConsult is an effective tool for PCP access to HC specialists. Identifiable knowledge gaps emerge that could be used to enhance continuing medical education and drive innovative HC service delivery.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".