MétaCan
Menu
Back to cohort

Randomized, Controlled Trial Comparing Mitral Valve Repair With Leaflet Resection Versus Leaflet Preservation on Functional Mitral Stenosis

2020· article· en· W3090786967 on OpenAlexafffund
Vincent Chan, C. David Mazer, Faeez Mohamad Ali, Adrian Quan, Marc Ruel, Benoit E. de Varennes, Alexander J. Gregory, Denis Bouchard, Richard Whitlock, Michael Chu, Aleksander Dokollari, Thierry Mesana, Deepak L. Bhatt, David A. Latter, Fei Zuo, Wendy Tsang, Hwee Teoh, Peter Jüni, Howard Leong‐Poi, Subodh Verma

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsInstitute of Health Services and Policy ResearchLondon Health Sciences CentreMcMaster UniversityOttawa Public HealthPopulation Health Research InstituteMontreal Heart InstituteUniversity of CalgaryRoyal Victoria HospitalUniversity of TorontoWestern UniversitySt. Michael's HospitalLibin Cardiovascular Institute of AlbertaUniversity of Ottawa
FundersInstitut de Cardiologie de MontréalMcGill University Health CentreHamilton Health SciencesUniversity Health NetworkMcGill UniversityUniversity of CalgaryUniversity of Ottawa
KeywordsMedicineStenosisMitral regurgitationSurgeryPerioperativeMitral valveRandomized controlled trialMitral valve repairCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Equipoise exists between the use of leaflet resection and preservation for surgical repair of mitral regurgitation caused by prolapse. We therefore performed a randomized, controlled trial comparing these 2 techniques, particularly in regard to functional mitral stenosis. Methods: One hundred four patients with degenerative mitral regurgitation surgically amenable to either leaflet resection or preservation were randomized at 7 specialized cardiac surgical centers. Exclusion criteria included anterior leaflet or commissural prolapse, as well as a mixed cause for mitral valve disease. Using previous data, we determined that a sample size of 88 subjects would provide 90% power to detect a 5–mm Hg difference in mean mitral valve gradient at peak exercise, assuming an SD of 6.7 mm with a 2-sided test with α=5% and 10% patient attrition. The primary end point was the mean mitral gradient at peak exercise 12 months after repair. Results: Patient age, proportion who were female, and Society of Thoracic Surgeons risk score were 63.9±10.4 years, 19%, and 1.4±2.8% for those who were assigned to leaflet resection (n=54), and 66.3±10.8 years, 16%, and 1.9±2.6% for those who underwent leaflet preservation (n=50). There were no perioperative deaths or conversions to replacement. At 12 months, moderate mitral regurgitation was observed in 3 subjects in the leaflet resection group and 2 in the leaflet preservation group. The mean transmitral gradient at 12 months during peak exercise was 9.1±5.2 mm Hg after leaflet resection and 8.3±3.3 mm Hg after leaflet preservation ( P =0.43). The participants had similar resting peak (8.3±4.4 mm Hg versus 8.4±2.6 mm Hg; P =0.96) and mean resting (3.2±1.9 mm Hg versus 3.1±1.1 mm Hg; P =0.67) mitral gradients after leaflet resection and leaflet preservation, respectively. The 6-minute walking distance was 451±147 m for those in the leaflet resection versus 481±95 m for the leaflet preservation group ( P =0.27). Conclusions: In this adequately powered randomized trial, repair of mitral prolapse with either leaflet resection or leaflet preservation was associated with similar transmitral gradients at peak exercise at 12 months postoperatively. These data do not support the hypothesis that a strategy of leaflet resection (versus preservation) is associated with a risk of functional mitral stenosis. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier NCT02552771.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.068
GPT teacher head0.320
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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

Citations39
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCirculationSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207