Enhancing variant of uncertain significance (VUS) interpretation in neurogenetics: collaborative experiences from a tertiary care centre
Bibliographic record
Abstract
BACKGROUND: The findings of variants of uncertain significance (VUS) on a clinical genetic testing report pose a challenge for attending healthcare professionals (HCPs) in patient care. Here, we describe the outcomes of multidisciplinary VUS Rounds, implemented at a neurological disease tertiary care centre, which aid in interpreting and communicating VUS identified in our neurogenetics patient population. METHODS: VUS Rounds brought together genetic counsellors, molecular geneticists and scientists to evaluate VUS against genomic and phenotypic evidence and assign an internal temperature classification of 'VUS Hot', 'True VUS' or 'VUS Cold', corresponding to potential pathogenicity. Biweekly meetings were held among the committee to deliberate variant classifications, determine additional clinical management actions and discuss nuances of VUS result communication. RESULTS: In total, 143 VUS identified in 72 individuals with neurological disease were curated between October 2022 and December 2023. Of these, 12.6% were classified as VUS Hot, carried by 22.2% of the individuals, allowing for prioritisation of additional evaluation to determine potential pathogenicity of the variants, such as clinical follow-up or segregation analysis. In contrast, 45.4% of VUS were Cold and could be eliminated from further consideration in the carrier's care. We thoroughly evaluated the various evidence that contributed to our VUS classifications and resulting clinical actions. CONCLUSIONS: The assessment of VUS leveraging multidisciplinary collaboration allowed us to delineate required follow-up analyses for our neurology patient population. Integration of VUS Rounds into healthcare practices ensures equitable knowledge dissemination among HCPs and effective incorporation of uncertain genetic results into patient care.
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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".