The impact of genetic counselor involvement in genetic and genomic test order review: A scoping review
Bibliographic record
Abstract
PURPOSE: The increasing complexity of genetic technologies paired with more genetic tests being ordered by nongenetic health care providers, has resulted in an increase in the number of inappropriately ordered tests. Genetic counselors (GCs) are ideally suited to assess the appropriateness of a genetic test. METHODS: We performed a scoping review of GC involvement in utilization management initiatives in order to describe the impact of having GCs involved in this process. Five databases (MEDLINE, EMBASE, CINHAL, EBM reviews, and Web of Science Core Collection) and gray literature were searched. We considered literature published in English since 2010. RESULTS: A total of 51 studies were included. The most commonly evaluated outcomes included cancellation rate, economic efficiencies, impact on medical management, diagnostic rate, and time or triage efficiencies. Several studies also described GC impact on nongenetic health care providers. CONCLUSION: Employment of GCs in the laboratory has been implemented widely as a solution to test misordering. These studies describe ways in which GCs can be integrated into testing workflows to reduce the number of inappropriate tests and have wider impacts on nongenetic health care providers' ordering practices and the patient experience.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".