Rapid right ventricular pacing for balloon valvuloplasty in congenital aortic stenosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Balloon aortic valvuloplasty (BAV) is a well-established treatment modality for congenital aortic valve stenosis. AIM: To evaluate the role of rapid right ventricular pacing (RRVP) in balloon stabilization during BAV on aortic regurgitation (AR) in pediatric patients. METHODS: A systematic review of the MEDLINE, Cochrane Library, and Scopus databases was conducted according to the PRISMA guidelines (end-of-search date: July 8, 2020). The National Heart, Lung, and Blood Institute and Newcastle-Ottawa scales was utilized for quality assessment. RESULTS: Five studies reporting on 72 patients were included. The studies investigated the use of RRVP-assisted BAV in infants (> 1 mo) and older children, but not in neonates. Ten (13.9%) patients had a history of some type of aortic valve surgical or catheterization procedure. Before BAV, 58 (84.0%), 7 (10.1%), 4 (5.9%) patients had AR grade 0 (none), 1 (trivial), 2 (mild), respectively. After BAV, 34 (49.3%), 6 (8.7%), 26 (37.7%), 3 (4.3%), patients had AR grade 0, 1, 2, and 3 (moderate), respectively. No patient developed severe AR after RRVP. One (1.4%) developed ventricular fibrillation and was defibrillated successfully. No additional arrhythmias or complications occurred during RRVP. CONCLUSION: RRVP can be safely used to achieve balloon stability during pediatric BAV, which could potentially decrease AR rates.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".