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Functional Quality of Life Following Open Valve Surgery in High-Risk Octogenarians

2012· article· en· W2026312936 on OpenAlexaff
Stephane Leung Wai Sang, Rakesh Chaturvedi, Sameena Iqbal, Kevin Lachapelle, Benoît de Varennes

Bibliographic record

VenueJournal of Cardiac Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Activities of daily livingAortic valve replacementSurgeryPhysical therapyInternal medicineStenosisNursing

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to determine the midterm functional quality of life in octogenarians after open valvular surgery. METHODS: One hundred and eighty-five consecutive patients above age 80 had valvular surgery with or without coronary artery bypass grafting (CABG). Using the Karnofsky Performance score and Barthel Index, patients were evaluated for functional autonomy, living disposition, and leisure activity by a single telephone interview. Subgroup analysis was performed on the 49 cases of isolated aortic valve replacement (AVR). RESULTS: Mean age of octogenarians undergoing valvular surgery was 82.7 years (range 80 to 92 years). Actuarial survival at one and three years was 71% and 59%, respectively, for the entire group, compared to 84% and 71%, respectively, for isolated AVRs. After a mean follow-up of 38 months there were 110 survivors (59.5%). Among survivors, 66% were autonomous, 26% semiautonomous, and 8% deemed dependent. Seventy-two percent were living at home, 19% in a residence, and 9% in a supervised nursing facility. Over 90% of patients pursued leisure activities in the social, cognitive, and physical domains. CONCLUSIONS: Valvular surgery in high-risk octogenarians, can be performed with acceptable mortality rates, and provide patients with functional autonomy and an excellent quality of life.

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)0.006
metaresearch head score (Gemma)0.002
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 score0.024
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.009
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.059
GPT teacher head0.356
Teacher spread0.296 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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