Prestenting for prevention of melody valve stent fractures: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: The role of right ventricular outflow tract (RVOT) prestenting in the prevention of Melody valve stent fractures (SFs) is not well defined. We aimed to perform a systematic review and meta-analysis comparing the incidence of SF in Melody valve transcatheter pulmonary implants with and without prestenting. METHODS: PubMed, EMBASE, and Cochrane Central were searched for studies that reported the incidence of SF in Melody valve transcatheter pulmonary implants stratified by the presence or absence of RVOT prestenting. Subgroup analyses were performed for (1) SF associated with a loss of stent integrity and (2) SF requiring reintervention. RESULTS: Five studies and 360 patients were included, of whom 207 (57.5%) received prestenting. Follow-up ranged from 15 to 30 months. SF were significantly reduced in the prestenting group (16.7%) when compared to no prestenting (33.5%) (odds-ratio [OR] 0.39; 95%CI 0.22-0.69). Patients who received prestenting also had a lower incidence of (1) SF associated with loss of stent integrity (OR 0.16; 95%CI 0.05-0.48) and (2) SF requiring reintervention (OR 0.15; 95%CI 0.02-0.91). CONCLUSION: Our findings suggest that stenting of the RVOT prior to Melody valve implantation is associated with a reduction in the incidence of SF and fracture-related reinterventions.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".