Mainstreaming of clinical genetic testing: A conceptual framework
Bibliographic record
Abstract
PURPOSE: Demand for genetic testing is increasing across medicine, whereas the genetics workforce remains stable. In response, mainstreaming models are being introduced, in which nongeneticist clinicians are increasingly involved in the genetic testing pathway. Because a standardized approach would facilitate evaluation and optimal patient care, a unified framework is warranted. METHODS: Through a focus group with clinical genetics experts, a conceptual framework for the mainstreaming of clinical genetic testing is proposed. Through a consensus process, experts elucidated the steps in the diagnostic care pathway and defined a set of variables that influence which mainstreaming model is best suited to specific patient care scenarios. RESULTS: A total of 35 individuals representing 20 distinct clinical genetics services and all Canadian provinces participated in the development of the framework. The framework describes 4 generalizable mainstreaming models of care, each with varying levels of involvement of the clinical genetics service in the diagnostic care pathway. CONCLUSION: This framework will help guide clinical teams in the design and evaluation of mainstreaming efforts. It is critical that these programs are evaluated and shared in a standardized way so that we can implement strategies that allow optimal utilization of genetics resources and improve patient care.
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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 | 0.042 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".