Bibliographic record
Abstract
An ever-growing catalog of human variants is hosted in the ClinVar database. In this database, submissions on a variant are combined into a multisubmitter record; and in the case of discordance in variant classification between submitters, the record is labeled as conflicting. The current study used ClinVar data to identify characteristics that would make variants more likely to be associated with the conflict class of variants. Furthermore, the Extreme Gradient Boosting algorithm was used to train classifier models to provide prediction of classification discordance for single submission variants in ClinVar database. Population allele frequency, the gene harboring the variant, variant type, consequence on protein, variant deleteriousness score, first submitter identity, and submission count were associated with conflict in variant classification. Using such features, the optimized classifier showed accuracy on the test set of 88% with the weighted average of precision, recall, and f1-score of 0.84, 0.88, and 0.85, respectively. There were pronounced associations between variant classification discordance and allele frequency, gene type, and the identity of the first submitter. The study provides the predicted discordance status for single-submitter variants deposited in ClinVar. This approach can be used to assess whether single-submitter variants are likely to be supported, or in conflict with, future entries; this knowledge may help laboratories with clinical variant assessment.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".