Non-geneticist champions are essential to the mainstreaming of genomic medicine
Bibliographic record
Abstract
Demand for genomic testing is increasing across medicine as it paves the way for earlier diagnoses and targeted management of patients with rare diseases. The diagnostic utility of genomic testing has been clearly established, with evidence demonstrating value of its earlier positioning in the genetic care pathway [ 1 , 2 ]. However, clinical genetics services are experiencing unsustainable pressures and are challenged to meet this growing need [ 3 , 4 ]. In response, the roles of genetics professionals are shifting, with more working in spaces outside of the genetics service [ 5 , 6 , 7 ]. Recent studies have also illustrated an additional solution: greater involvement of non-geneticist clinicians in the delivery of genomic medicine [ 8 , 9 ]. Concurrently, institutions and governments are becoming increasingly aware of the potential benefits of such “mainstreaming”—where non-geneticist clinicians are responsible for components of the genetic care pathway—and are supporting expanded and earlier access to these important tests [ 10 ]. To increase access, however, intentional and purpose-built strategies are needed to increase capacity and facilitate adoption into the practice of non-geneticist clinicians.
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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 | 0.040 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".