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Longitudinal Outcomes Following Mitral Valve Repair for Infective Endocarditis

2024· article· en· W4402149237 on OpenAlexaffabout
Yuan Qiu, Lawrence Lau, Zaim Khan, David Messika–Zeitoun, Marc Ruel, Vincent Chan

Bibliographic record

VenueMicroorganisms · 2024
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInfective endocarditisMitral valve repairMedicineMitral valveEndocarditisSurgeryCardiologyDebridement (dental)Internal medicineMitral regurgitation

Abstract

fetched live from OpenAlex

Mitral valve repair is the ideal approach in managing mitral valve infective endocarditis for patients requiring surgery. However, viable repair is influenced by the extent of valve destruction and there can be technical challenges in reconstruction following debridement. Overall, data describing long-term outcomes following mitral repair of infective endocarditis are scarce. We, therefore, assessed the late outcomes of 101 consecutive patients who underwent mitral valve repair for IE at the University of Ottawa Heart Institute from 2001 to 2021. The 5- and 10-year survival rate was 80.8 ± 4.7% and 61.2 ± 9.2%, respectively. Among these 101 patients, 7 ultimately required mitral valve reoperation at a median of 5 years after their initial operation. These patients were of a mean age of 35.9 ± 7.3 years (range 22-44 years) at the time of their initial operation. The 5- and 10-year freedom from mitral valve reoperation was 93.6 ± 3.4% and 87.7 ± 5.2%, respectively. Overall, mitral valve repair can be an effective method for treating infective endocarditis with a favourable freedom from reoperation and mortality over the long term.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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