End‐Recovery QTc: A Useful Metric for Assessing Genetic Variants of Unknown Significance in Long‐QT syndrome
Bibliographic record
Abstract
INTRODUCTION: Genetic variants represent benign single-nucleotide polymorphisms, disease causing mutations or variants of unknown significance (VUS). Resting, exercise, and recovery QTc intervals have been utilized to detect long-QT syndrome (LQTS) mutations. We sought to provide clinical data that may assist in classifying the presented VUS as disease causing/benign and to determine whether resting and/or end-recovery QT parameters can evaluate the significance of VUS. METHODS AND RESULTS: Twenty-six patients with a VUS in genes associated with LQTS (15 females, age 38 ± 16 years) and 26 age and gender matched controls (age 37 ± 20 years) were included. There were 10 VUS (5 KCNQ1, 4 KCNH2, 1 KCNE1) in 12 families. All but 1 VUS was associated with sudden cardiac death (SCD), aborted SCD or Torsade de pointes. A Schwartz score of ≥3.5 was observed in at least 1 family member with each VUS. Resting QTc was marginally longer in VUS patients compared with controls (458 ± 48 vs 437 ± 25, P = 0.052). A prolonged resting QTc (>470 ms males, >480 ms females) identified 6 VUS carriers and 1 control. VUS carriers had a substantially longer end-recovery QTc (502 ± 68 vs 427 ± 17, P < 0.01) with an end-recovery QTc > 445 ms in 20/26 VUS patients compared to 2/26 controls (P < 0.01). The area under the receiver operating characteristic curve for resting QTc was 0.68 (95% CI, 0.53-0.83, P = 0.03) compared to the end-recovery QTc of 0.88 (95% CI, 0.76-0.99, P < 0.0001). CONCLUSION: Variants in the current study appear to be disease causing. The end-recovery QTc is a useful metric when interpreting LQT VUS.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".