Predicting prolonged intensive care unit length of stay in patients undergoing coronary artery bypass surgery - development of an entirely preoperative scorecard
Bibliographic record
Abstract
We sought to develop a predictive model based exclusively on preoperative factors to identify patients at risk for PrlICULOS following coronary artery bypass grafting (CABG). Retrospective analysis was performed on patients undergoing isolated CABG at a single center between June 1998 and December 2002. PrlICULOS was defined as initial admission to ICU exceeding 72 h. A parsimonious risk-predictive model was constructed on the basis of preoperative factors, with subsequent internal validation. Of 3483 patients undergoing isolated CABG between June 1998 and December 2002, 411 (11.8%) experienced PrlICULOS. Overall in-hospital mortality was higher among these patients (14.4% vs. 1.2%, P<or=0.0001). The following variables were found to be independent predictors of PrlICULOS: increased age, recent myocardial infarction, preoperative renal failure, cerebral and/or peripheral vascular disease, chronic obstructive pulmonary disease, ejection fraction <40%, previous CABG, triple-vessel and/or left main disease, NYHA class IV symptoms and urgent or emergent status. Subsequent validation of this model demonstrated a c-statistic of 78%. An internally-validated, risk predictive model of PrlICULOS in patients undergoing CABG was constructed. Based on preoperative clinical factors, a scorecard was developed allowing identification of these patients prior to surgery and allowing for strategies aimed at optimizing hospital resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".