Pacemaker Lead Prolapse through the Pulmonary Valve in Children
Bibliographic record
Abstract
BACKGROUND: Transvenous pacemaker leads in children are often placed with redundant lead length to allow for anticipated patient growth. This excess lead may rarely prolapse into the pulmonary artery and potentially interfere with valve function. We sought to determine the response to lead repositioning on pulmonary valve insufficiency. METHODS: Retrospective reviews of demographics, lead type, implant duration, and radiography and echocardiography. RESULTS: A total of 11 pediatric patients were identified with lead prolapse through the pulmonary valve, of which nine patients underwent procedures to retract and reposition the lead (age at implant 9 +/- 4 years, age at revision 13 +/- 4 years). The implant duration prior to revision was 4 +/- 3 years. Two leads required radiofrequency extraction sheaths for removal, two pulled back using a snare, while five leads were simply retracted and repositioned. Tricuspid regurgitation was none/trivial (three), mild (four), or moderate (two) and only two improved with repositioning or replacement. Pulmonary regurgitation preoperatively was mild (three), mild-moderate (two), or moderate (four) compared with trivial (three), mild (four), and moderate (two) after revision. Patients with longer-term implanted leads had less improvement in pulmonary insufficiency. Two patients had mild pulmonary stenosis from lead-related obstruction. CONCLUSIONS: Prolapse of transvenous pacing leads into the pulmonary artery can occur when excess slack is left for growth. Leads can often be repositioned, but may require extraction and replacement, particularly if chronically implanted and adherent to valve apparatus. Lead revision does not always resolve pulmonary insufficiency, potentially leaving permanent valve damage.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".