Implantable Cardioverter-Defibrillators in Tetralogy of Fallot
Bibliographic record
Abstract
BACKGROUND: Tetralogy of Fallot is the most common form of congenital heart disease in implantable cardioverter-defibrillator (ICD) recipients, yet little is known about the value of ICDs in this patient population. METHODS AND RESULTS: We conducted a multicenter cohort study in high-risk patients with Tetralogy of Fallot to determine actuarial rates of ICD discharges, identify risk factors, and characterize ICD-related complications. A total of 121 patients (median age 33.3 years; 59.5% male) were enrolled from 11 sites and followed up for a median of 3.7 years. ICDs were implanted for primary prevention in 68 patients (56.2%) and for secondary prevention in 53 (43.8%), defined by clinical sustained ventricular tachyarrhythmia or resuscitated sudden death. Overall, 37 patients (30.6%) received at least 1 appropriate and effective ICD discharge, with a median ventricular tachyarrhythmia rate of 213 bpm. Annual actuarial rates of appropriate ICD shocks were 7.7% and 9.8% in primary and secondary prevention, respectively (P=0.11). A higher left ventricular end-diastolic pressure (hazard ratio 1.3 per mm Hg, P=0.004) and nonsustained ventricular tachycardia (hazard ratio 3.7, P=0.023) independently predicted appropriate ICD shocks in primary prevention. Inappropriate shocks occurred in 5.8% of patients yearly. Additionally, 36 patients (29.8%) experienced complications, of which 6 (5.0%) were acute, 25 (20.7%) were late lead-related, and 7 (5.8%) were late generator-related complications. Nine patients died during follow-up, which corresponds to an actuarial annual mortality rate of 2.2%, which did not differ between the primary and secondary prevention groups. CONCLUSIONS: Patients with tetralogy of Fallot and ICDs for primary and secondary prevention experience high rates of appropriate and effective shocks; however, inappropriate shocks and late lead-related complications are common.
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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 | 0.001 | 0.000 |
| 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".