Repolarization Dynamics During Exercise Discriminate Between LQT1 and LQT2 Genotypes
Bibliographic record
Abstract
UNLABELLED: Genotype and Exercise in LQTS. BACKGROUND: Repolarization dynamics during exercise in patients with long-QT Syndrome (LQTS) may be influenced by various factors such as a patient's genotype. We sought to systematically characterize the repolarization dynamics during exercise in patients with LQTS with a particular focus on the influence of genotype. METHODS: Three groups of patients were studied on the basis of clinical status and genotype: LQT1, LQT2, and normal controls. Twenty-five age- and gender-matched patients were selected for each group. The QTc was measured during bicycle exercise testing and its dynamics were compared between the 3 groups. RESULTS: The degree of QTc prolongation during exercise was greater in LQTS patients (LQT1 80 ± 47 ms, LQT2 64 ± 41 ms, Control 46 ± 20 ms, P = 0.02), with significant differences between LQT1 and LQT2 patients observed at heart rates ≥ 60% of the predicted maximum (P < 0.05). LQT1 patients demonstrated progressive or persistent QTc prolongation at higher heart rates, whereas LQT2 patients demonstrated maximum QTc prolongation at submaximal heart rates (50% of the predicted maximum) with subsequent QTc correction toward baseline values at higher heart rates. Importantly, these observations were consistent regardless of age, gender, or exercise type in subgroup analyses. CONCLUSIONS: Reduced repolarization reserve in LQTS is genotype and heart rate specific.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".