MétaCan
Menu
Back to cohort
Record W2113822022 · doi:10.1503/cmaj.050053

Assessing the risk of waiting for coronary artery bypass graft surgery among patients with stenosis of the left main coronary artery

2005· article· en· W2113822022 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Medical Association Journal · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineStenosisArteryCoronary artery bypass surgeryTriageLogistic regressionCardiologyInternal medicineCoronary artery diseaseSurgeryEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Significant controversy remains over how urgently coronary artery bypass graft surgery (CABG) should be scheduled, particularly for patients with stenosis of the left main coronary artery. Our main objective was to evaluate the safety of waiting for CABG among patients with left main coronary artery disease using a standardized triage system. METHODS: We identified 561 consecutive patients with stenosis of the left main coronary artery who were scheduled to undergo CABG between Apr. 1, 1999, and Mar. 31, 2003. Using standardized triage criteria, patients were assigned to 1 of 4 waiting queues: "emergent," "in-hospital urgent," "out-of-hospital semi-urgent A" and "out-of-hospital semi-urgent B." Postoperative outcome measures were in-hospital death from any cause and a composite outcome measure of in-hospital death from any cause, a prolonged requirement for postoperative mechanical ventilation (> 24 h) and a prolonged postoperative hospital stay (> 9 d). Waiting-time variables included the specific queue, whether patients waited longer than the standard time established for each queue and whether patients were upgraded to a more urgent queue. Logistic regression analysis was used to identify independent predictors of the composite outcome; propensity scores (probability of being assigned to a specific queue) were entered into the model to adjust for patient variability among queues. RESULTS: Of the 561 patients, 65 (11.6%) were assigned to the emergent group, 343 (61.1%) to the in-hospital urgent group, 91 (16.2%) to the semi-urgent A queue and 62 (11.1%) to the semi-urgent B queue. Four patients (0.7%) died while waiting for surgery. The median waiting times were as follows: emergent group, 0 days; in-hospital urgent group, 2 days; 30 days in the semi-urgent A group and 49 days in the semi-urgent B group. A total of 52 patients (9.3%) were upgraded to a more urgent queue, and 147 patients (26.2%) waited longer than the standard times for their respective queue. The overall in-hospital mortality was 5.5% (n = 31), and the composite outcome was 32.6% (n = 183). Independent predictors of the composite outcome were myocardial infarction within 7 days before surgery, preoperative renal failure, ejection fraction of less than 40%, age greater than 70 years and stenosis of left main coronary artery greater than 70%. Waiting-time variables were associated with neither a significantly higher mortality nor morbidity outcome. INTERPRETATION: For selected patients with stenosis of the left main coronary artery, waiting for CABG did not appear to be associated with increased mortality or morbidity.

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.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.311
Teacher spread0.287 · 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