Abstract 17736: ICD Lead Performance in Athletes: Long-term Results of a Prospective Multinational Registry
Bibliographic record
Abstract
Background: Athletic activity, especially including those with repetitive upper extremity motion, may increase the risk of lead failure in those with an implanted defibrillator (ICD). A prospective international registry has followed athletes with ICDs. The current study is an analysis of this registry focusing on lead failures. Hypothesis: Lead failures will be increased in those participating in sports. Methods: Athletes with transvenous ICDs (age 10-60 years) participating in sports were enrolled in a prospective international registry. Contact sports were defined based on American Academy of Pediatrics definitions. Clinical outcomes including lead failure (nonphysiologic noise or significant changes in sensing or pacing) were adjudicated by two electrophysiologists. Results: The registry enrolled 440 athletes with an ICD. Median age was 33 years (111 Conclusions: Overall lead failure rate in non-recalled leads (89.0%% over ten years) is similar to that reported in the general population. In this cohort of 440 athletes, intense arm activity and contact sports did not predict lead failure compared to those not engaged in these activities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".