Effect of beta-blockers on QT dynamics in the long QT syndrome: measuring the benefit
Bibliographic record
Abstract
AIMS: Beta-blockers are the standard of care for the treatment of long QT syndrome (LQTS), and have been shown to reduce recurrent syncope and mortality in patients with type 1 LQTS (LQT1). Although beta-blockers have minimal effect on the resting corrected QT interval, their effect on the dynamics of the non-corrected QT interval is unknown, and may provide insight into their protective effects. METHODS AND RESULTS: Twenty-three patients from eight families with genetically distinct mutations for LQT1 performed exercise stress testing before and after beta-blockade. One hundred and fifty-two QT, QTc, and Tpeak-Tend intervals were measured before starting beta-blockers and compared with those at matched identical cycle lengths following beta-blockade. Beta-blockers demonstrated heart-rate-dependent effects on the QT and QTc intervals. In the slowest heart rate tertile (<90 b.p.m.), beta-blockade increased the QT and QTc intervals (QT: 405 vs. 409 ms; P = 0.06; QTc: 459 vs. 464 ms; P = 0.06). In the fastest heart rate tertile (>100 b.p.m.), the use of beta-blocker was associated with a reduction in both the QT and QTc intervals (QT: 367 vs. 358 ms; P < 0.0001; QTc: 500 vs. 486 ms; P < 0.0001). The Tpeak-Tend interval showed minimal change at slower heart rates (<90 b.p.m.) (93 vs. 87 ms; P = 0.09) and at faster heart rates (>100 b.p.m.) (87 vs. 84 ms; P = NS) following beta-blockade. CONCLUSION: Beta-blockers have heart-rate-dependent effects on the QT and QTc intervals in LQTS. They appear to increase the QT and QTc intervals at slower heart rates and shorten them at faster heart rates during exercise.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".