Genetic variant annotation scores in congenital long <scp>QT</scp> syndrome
Bibliographic record
Abstract
BACKGROUND: Congenital Long QT Syndrome (LQTS) is a hereditary arrhythmic disorder. We aimed to assess the performance of current genetic variant annotation scores among LQTS patients and their predictive impact. METHODS: We evaluated 2025 patients with unique mutations for LQT1-LQT3. A patient-specific score was calculated for each of four established genetic variant annotation algorithms: CADD, SIFT, REVEL, and PolyPhen-2. The scores were tested for the identification of LQTS and their predictive performance for cardiac events (CE) and life-threatening events (LTE) and then compared with the predictive performance of LQTS categorization based on mutation location/function. Score performance was tested using Harrell's C-index. RESULTS: A total of 917 subjects were classified as LQT1, 838 as LQT2, and 270 as LQT3. The identification of a pathogenic variant occurred in 99% with CADD, 92% with SIFT, 100% with REVEL, and 86% with PolyPhen-2. However, none of the genetic scores correlated with the risk of CE (Harrell's C-index: CADD = 0.50, SIFT = 0.51, REVEL = 0.50, and PolyPhen-2 = 0.52) or LTE (Harrell's C-index: CADD = 0.50, SIFT = 0.53, REVEL = 0.54, and PolyPhen-2 = 0.52). In contrast, high-risk mutation categorization based on location/function was a powerful independent predictor of CE (HR = 1.88; p < .001) and LTE (HR = 1.89, p < .001). CONCLUSION: In congenital LQTS patients, well-established algorithms (CADD, SIFT, REVEL, and PolyPhen-2) were able to identify the majority of the causal variants as pathogenic. However, the scores did not predict clinical outcomes. These results indicate that mutation location/functional assays are essential for accurate interpretation of the risk associated with LQTS mutations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".