Moving toward more consistency in variant classification and clinical action
Bibliographic record
Abstract
Genetics professionals understand that variant classifications are not static and can change over time. Reclassifications may occur in response to changes in evidence, new or modified approaches to weighing the evidence, and/or changes in the overall systems used to classify variants. How should other laboratories be notified about proposed reclassifications or even the original classifications from a particular laboratory? How should consensus among laboratories be reached? Should variant classifications be routinely reviewed by laboratories in a proactive manner as a form of continuous quality improvement? If so, should all variants or only variants of uncertain significance be proactively reviewed, or should the system remain largely reactive, with reclassifications being mainly clinician or patient initiated? Under what circumstances should the referring health care provider be notified and the patient recontacted for recounseling? These are some of the important questions tackled in the article “Reclassification of clinically-detected sequence variants: Framework for genetic clinicians and clinical scientists by CanVIG-UK (Cancer Variant Interpretation Group UK)” in the September issue of Genetics in Medicine.
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 | 0.681 | 0.711 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.022 | 0.010 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.035 | 0.033 |
| Open science | 0.016 | 0.036 |
| Research integrity | 0.018 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".