Does anemia impact hospital readmissions after coronary artery bypass surgery?
Bibliographic record
Abstract
BACKGROUND: Anemia is one of the most common complications of coronary artery bypass graft (CABG) surgery and has been shown to be associated with increased morbidity and mortality. The impact of anemia on hospital readmission after CABG, a potential measure of delayed complications, has not been addressed. STUDY DESIGN AND METHODS: We conducted a single-center retrospective study of 2102 patients who had CABG in Ontario to determine whether anemia at hospital discharge was associated with increased 30-day hospital readmissions, readmission secondary to cardiac disease, and 30-day mortality using administrative data. RESULTS: Of the 2102 patients, 224 patients (11%) were readmitted within 30 days of hospital discharge. Infection was the leading cause of readmissions (24%), followed by heart failure (13%), pulmonary disease (7%), and hemorrhagic disease (7%). Overall, 2.6% of patients were readmitted because of cardiac disease. Of patients discharged, 48% were discharged with a hemoglobin (Hb) level between 8 and 10 g/dL and 42% between 10 and 12 g/dL. Predischarge Hb concentration was not a significant independent predictor of 30-day readmission to the hospital due to all causes, readmission to the hospital due to cardiac causes, or 30-day mortality. A higher comorbidity score, adjusted odds ratio (OR) of 2.1 (95% confidence interval [CI], 1.3-3.6), leg and sternal wound infections OR of 1.9 (95% CI, 1.2-3.0), and postoperative renal failure OR of 1.4 (95% CI, 1.2-2.0) were associated with increased 30-day readmission rates. CONCLUSIONS: The predischarge Hb concentration after CABG was not associated with 30-day readmissions.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".