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Impact of Prosthesis-Patient Mismatch on Survival After Mitral Valve Replacement

2007· article· en· W2147253150 on OpenAlexaff
Julien Magné, Patrick Mathieu, Jean G. Dumesnil, David Tanné, François Dagenais, Daniel Doyle, Philippe Pîbarot

Bibliographic record

VenueCirculation · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineHazard ratioProsthesisConfidence intervalMitral valve replacementMitral valveInternal medicineImplantSurgeryCardiologyProspective cohort studySurvival rate

Abstract

fetched live from OpenAlex

BACKGROUND: We recently reported that valve prosthesis-patient mismatch (PPM) is associated with persisting pulmonary hypertension after mitral valve replacement. Thus, the objective of this study was to evaluate the impact of PPM on mortality in patients undergoing mitral valve replacement. METHODS AND RESULTS: The indexed valve effective orifice area was estimated for each type and size of prosthesis being implanted in 929 consecutive patients and used to define PPM as not clinically significant if > 1.2 cm2/m2, as moderate if > 0.9 and < or = 1.2 cm2/m2, and as severe if < or = 0.9 cm2/m2. Moderate PPM was present in 69% of patients; severe PPM was seen in 9%. For patients with severe PPM, 6-year survival (74+/-5%) and 12-year survival (63+/-7%) were significantly less than for patients with moderate PPM (84+/-1% and 76+/-2%; P=0.027) or nonsignificant PPM (90+/-2% and 82+/-4%; P=0.002). On multivariate analysis, severe PPM was associated with higher mortality (hazard ratio, 3.2; 95% confidence interval, 1.5 to 6.8; P=0.003). CONCLUSIONS: Severe PPM is an independent predictor of mortality after mitral valve replacement. As opposed to other independent risk factors, PPM may be avoided or its severity may be reduced with the use of a prospective strategy at the time of operation. For patients identified as being at risk for severe PPM, every effort should be made to implant a prosthesis with a larger effective orifice area.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.007
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.015
GPT teacher head0.337
Teacher spread0.322 · 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.

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

Citations151
Published2007
Admission routes1
Has abstractyes

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