Variant Reinterpretation in Survivors of Cardiac Arrest With Preserved Ejection Fraction (the Cardiac Arrest Survivors With Preserved Ejection Fraction Registry) by Clinicians and Clinical Commercial Laboratories
Bibliographic record
Abstract
BACKGROUND: Following an unexplained cardiac arrest, clinical genetic testing is increasingly becoming standard of care. Periodic review of variant classification is required, as reinterpretation can change the diagnosis, prognosis, and management of patients and their relatives. METHODS: This study aimed to develop and validate a standardized algorithm to facilitate clinical application of the 2015 American College of Medical Genetics and Association for Molecular Pathology guidelines for the interpretation of genetic variants. The algorithm was applied to genetic results in the Cardiac Arrest Survivors With Preserved Ejection Fraction Registry, to assess the rate of variant reclassification over time. Variant classifications were then compared with the classifications of 2 commercial laboratories to determine the rate and identify sources of variant interpretation discordance. RESULTS: =0.03). For the second part of the study, 50% (70 of 139) of variants had discrepant interpretations (excluding benign variants), provided by at least 1 team. CONCLUSIONS: Periodic review of genetic variant classification is a key component of follow-up care given rapidly changing information in the field. There is potential for clinical care gaps with discrepant variant interpretations, based on the interpretation and application of current guidelines. The development of gene- and disease-specific guidelines and algorithms may provide an opportunity to further standardize variant interpretation reporting in the future. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00292032.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".