Diabetes and Coronary Artery Bypass Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the adequacy of perioperative glycemic control in diabetic patients undergoing coronary artery bypass grafting (CABG) and to explore the association between glycemic control and in-hospital morbidity/mortality. RESEARCH DESIGN AND METHODS: Retrospective cohort study of consecutive patients with diabetes undergoing CABG between April 2000 and March 2001 who survived at least 24 h postoperatively. RESULTS: Of the 291 patients in this study, 95% had type 2 diabetes and 40% had retinopathy, nephropathy, or neuropathy at baseline. During hospitalization (median 7 days), 78 (27%) of these patients suffered a nonfatal stroke or myocardial infarction, septic complication, or died ("adverse outcomes"). Glycemic control was suboptimal (average glucose on first postoperative day was 11.4 [11.2-11.6] mmol/l) and was significantly associated with adverse outcomes post-CABG (P = 0.03). Patients whose average glucose level was in the highest quartile on postoperative day 1 had higher risk of adverse outcomes after the first postoperative day than those with glucose in the lowest quartile (odds ratio 2.5 [1.1-5.3]). Even after adjustment for other clinical and operative factors, average blood glucose level on the first postoperative day remained significantly associated with subsequent adverse outcomes: for each 1-mmol/l increase above 6.1 mmol/l, risk increased by 17%. CONCLUSIONS: Perioperative glycemic control in our cohort of diabetic patients undergoing CABG in a tertiary care facility was suboptimal. We believe closure of this care gap is imperative, because hyperglycemia in the first postoperative day was associated with subsequent adverse outcomes in our study patients.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".