The development of the Clinician-reported Genetic testing Utility InDEx (C-GUIDE): a novel strategy for measuring the clinical utility of genetic testing
Bibliographic record
Abstract
PURPOSE: Clinical utility describes a genetic test's value to patients, families, health-care providers, systems, or society. This study aims to define clinical utility from the perspective of clinicians and develop a novel outcome measure that operationalizes this concept. METHODS: Item selection for the Clinician-reported Genetic testing Utility InDEx (C-GUIDE) was informed by a scoping review of the literature. Item reduction was guided by qualitative and quantitative feedback from semistructured interviews and a cross-sectional survey of genetics and nongenetics specialists. Final item selection, index scoring, and structure were guided by feedback from an expert panel of genetics professionals. RESULTS: A review of 194 publications informed the selection of a preliminary set of 25 items. Feedback from 35 semistructured interviews, 113 surveys, and 11 expert panelists informed the content and wording of C-GUIDE's final set of 18 items that reflect on the utility of testing related to diagnosis, management, and familial/psychosocial impact. C-GUIDE achieves content and face validity for use in a range of diagnostic genetic testing settings. CONCLUSION: Work to establish reliability and construct validity is underway. C-GUIDE will be useful in comparative studies to generate policy-relevant evidence pertaining to the clinical utility of genetic testing across a range of settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".