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Record W2327448256 · doi:10.1177/021849230801600310

Predictors of Emergency Conversion to On-Pump during Off-Pump Coronary Surgery

2008· article· en· W2327448256 on OpenAlexaff
Arman Hovakimyan, Vilen Manukyan, Sarkis Ghazaryan, Merouzhan Saghatelyan, Lusine Abrahamyan, Hagop Hovaguimian

Bibliographic record

VenueAsian Cardiovascular and Thoracic Annals · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCardiopulmonary bypassIntensive care unitCardiologyOff-pump coronary artery bypassArterySurgeryInternal medicineBypass grafting

Abstract

fetched live from OpenAlex

The purpose of this study was to determine predictors and evaluate outcomes of emergency conversion to cardiopulmonary bypass during planned off-pump coronary artery bypass grafting. From January 2001 to November 2005, of 467 consecutive patients aged >or= 60 years who underwent off-pump coronary surgery, 17 (3.6%) were converted to cardiopulmonary bypass. Those converted to an on-pump technique had significantly higher rates of postoperative cerebrovascular accident (17.6% vs 1.1%), intraaortic balloon pumping (5.9% vs 0%), and red blood cell transfusion (82.4% vs 57.3%), as well as prolonged intensive care unit stay (52.9% vs 25.2%), ventilation time (25% vs 5.3%) and hospital stay (64.7% vs 31.3%) compared to patients whose operation was completed off-pump. Multivariable logistic regression identified left ventricular ejection and left main stenosis as significantly associated with conversion. The rate of emergency conversion to cardiopulmonary bypass during planned off-pump coronary surgery was acceptable, but patients who required conversion had less favorable early outcomes than those who remained off-pump.

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)0.000
metaresearch head score (Gemma)0.006
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.034
GPT teacher head0.285
Teacher spread0.251 · 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

Citations20
Published2008
Admission routes1
Has abstractyes

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Same venueAsian Cardiovascular and Thoracic AnnalsSame topicCardiac and Coronary Surgery TechniquesFrench-language works237,207