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Record W2332376917 · doi:10.1177/021849230501300210

Aortic Valve Replacement with Toronto SPV in Elderly Patients: 10-Year Results

2005· article· en· W2332376917 on OpenAlexaboutno aff
Muhammed Tamim, Thierry Bové, Yves Van Belleghem, Frank Caes, Katrien François, Guido J. Van Nooten

Bibliographic record

VenueAsian Cardiovascular and Thoracic Annals · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAortic valve replacementCardiologyInternal medicineHemodynamicsAortic valveBody mass indexRetrospective cohort studyPopulationProsthesisSurgeryStenosis

Abstract

fetched live from OpenAlex

A retrospective assessment of clinical and echocardiographic variables was performed in 145 patients who received a Toronto SPV aortic valve replacement. The majority (90%) of these elderly patients (mean age, 75.5 +/- 7.4 years) were preoperatively in New York Heart Association class III-IV. Operative mortality was 4.8%. Follow-up was complete up to 10 years and revealed few valve-related complications: thromboembolism (7), bleeding (4), and prosthesis dysfunction necessitating reoperation (3). Late mortality was cardiac-related in 11.7% and noncardiac-related in 17.2%. Actuarial survival was 83% at 5 years and 63% at 8 years. Echocardiography showed low transvalvular gradients (peak, 17.5 +/- 7.5 mm Hg; mean, 9.2 +/- 4.2 mm Hg) resulting in a significant reduction in left ventricular mass index during the first 3 years. Independent of the transprosthetic gradient, left ventricular mass index tended to increase again beyond the 5th year, which correlated positively with the presence of arterial hypertension in this older population. The Toronto SPV bioprosthesis offers an aortic valve substitute with excellent long-term hemodynamics, resulting in significant early left ventricular mass regression. Considering the limitations of this selected elderly population, the clinical outcome and survival up to 10 years are encouraging, with few observed valve-related events.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.014
GPT teacher head0.321
Teacher spread0.307 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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