Mexiletine in the treatment of LQT2, LQT3, and acquired LQTS: a meta-analysis
Bibliographic record
Abstract
Background: High mortality in patients with Long QT Syndrome (LQTS) can be reduced with proper treatment. Gene-specific therapy is crucial, as many treatments are not equally effective across different LQTS types. While mexiletine has been established in the treatment of LQT3, its use in other types of LQT need further evaluation. Methods: A meta-analysis was conducted using systematic electronic searches of PubMed, Embase, and Cochrane Library. We assessed QTc reduction and cardiac events after Mexiletine treatment. Inclusion criteria: any study with no language restriction that diagnoses any type of LQTS, uses mexiletine treatment, and provides QTc comparison before and after treatment. Animal studies were excluded. The NIH Study Quality Assessment Tools and Newcastle-Ottawa Scale were used to evaluate bias. Data were analyzed using Review Manager 5.4 and MedCalc software Results: Nine studies (n=281) were included. Mexiletine reduced QTc by -64ms (mean difference [MD], -64.22; 95% confidence interval [CI] -76.13 to -52.30; p<.001; I2 60%). Sensitivity and subanalyses showed consistent efficacy. In five studies (n=76), the number of patient with high-risk QTc (>500ms) significantly decreased (Risk Ratio [RR], 0.38; 95% CI 0.26-0.55; p<.001). Five studies (n=141) showed a significant reduction in cardiac events (RR, 0.25; 95% CI 0.14-0.44; p<.001). Two studies reported gastrointestinal (GI) problems and vertigo as side effects of mexiletine treatment. Conclusion: Mexiletine significantly reduces QTc and cardiac events in LQT2, LQT3, and aLQT patients. Mexiletine also significantly reduces the number of Long QT patients with high-risk QTc Funding: No external funding was received for this study Registration: CRD420250652574
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".