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Record W2074846636 · doi:10.1532/hsf98.20071058

Results of Adjunctive Coronary Endarterectomy in 548 Patients

2008· article· en· W2074846636 on OpenAlexaboutno aff
Serdar Akgün, C. Selim Isbir, Tekin Yıldırım, Ali Civelek, Sinan Arsan

Bibliographic record

VenueThe Heart Surgery Forum · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyCoronary arteriesInternal medicineMyocardial infarctionEndarterectomyAnginaCoronary artery diseaseCanadian Cardiovascular SocietyArteryAnastomosisSurgeryStenosis

Abstract

fetched live from OpenAlex

Coronary endarterectomy is a controversial procedure that plays a particular role in the treatment of coronary artery disease. We retrospectively investigated the results for 548 patients who underwent coronary endarterectomy as an adjunctive therapy for coronary artery bypass graft surgery during the period between 1996 and 2004. We assessed short-term outcomes and identified risk factors for adverse outcomes. Mean patient age was 67.9 + 9.3 years and mean angina class was 2.7 + 0.3. The mean number of distal anastomoses was 3.8 + 1.1 patients (73.4%) had single and 151 (27.6%) multiple coronary artery endarterectomies. Of the 151 patients who underwent multiple endarterectomies, 97 (17.7%) had endarterectomies in 2 coronary arteries, 40 (7.2%) in 3 coronary arteries, 11 (2%) in 4 coronary arteries, 2 (0.36%) in 5 coronary arteries, and 1 (0.18%) in 6 coronary arteries. Postoperative mortality was 6.2% (34 patients). The predictors for early mortality were recent myocardial infarction and left ventricular dysfunction. Our results suggest that adjunctive coronary endarterectomy can be accomplished with acceptable results but with higher mortality rates than ordinary coronary artery bypass grafting. Adjunctive coronary endarterectomy should be considered as a last option for the surgical treatment of diffuse coronary disease.

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)0.001
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 score0.094
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

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