T<sub>peak</sub>‐T<sub>end</sub>, T<sub>peak</sub>‐T<sub>end</sub>/<scp>QT</scp> ratio and T<sub>peak</sub>‐T<sub>end</sub> dispersion for risk stratification in Brugada Syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Brugada syndrome is an ion channelopathy that predisposes affected subjects to ventricular tachycardia/fibrillation (VT/VF), potentially leading to sudden cardiac death (SCD). Tpeak‐Tend intervals, (Tpeak‐Tend)/QT ratio and Tpeak‐Tend dispersion have been proposed for risk stratification, but their predictive values in Brugada syndrome have been challenged recently. Methods A systematic review and meta‐analysis was conducted to examine their values in predicting arrhythmic and mortality outcomes in Brugada Syndrome. PubMed and Embase databases were searched until 1 May 2018, identifying 29 and 57 studies. Results Nine studies involving 1740 subjects (mean age 45 years old, 80% male, mean follow‐up duration was 68 ± 27 months) were included. The mean Tpeak‐Tend interval was 98.9 ms (95% CI: 90.5‐107.2 ms) for patients with adverse events (ventricular arrhythmias or SCD) compared to 87.7 ms (95% CI: 80.5‐94.9 ms) for those without such events, with a mean difference of 11.9 ms (95% CI: 3.6‐20.2 ms, P = 0.005; I2 = 86%). Higher (Tpeak‐Tend)/QT ratios (mean difference = 0.019, 95% CI: 0.003‐0.036, P = 0.024; I2 = 74%) and Tpeak‐Tend dispersion (mean difference = 7.8 ms, 95% CI: 2.1‐13.4 ms, P = 0.007; I2 = 80%) were observed for the event‐positive group. Conclusion Tpeak‐Tend interval, (Tpeak‐Tend)/QT ratio and Tpeak‐Tend dispersion were higher in high‐risk than low‐risk Brugada subjects, and thus offer incremental value for risk stratification.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".