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Record W2120886750 · doi:10.1002/ccd.26235

Prestenting for prevention of melody valve stent fractures: A systematic review and meta‐analysis

2015· review· en· W2120886750 on OpenAlexaff
Rhanderson Cardoso, Mohammad Ansari, Daniel García, Satinder Sandhu, Derek R. Brinster, Nicolò Piazza

Bibliographic record

VenueCatheterization and Cardiovascular Interventions · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineMeta-analysisStentMEDLINESystematic reviewCardiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.067
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.119
GPT teacher head0.442
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations37
Published2015
Admission routes1
Has abstractyes

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