Diabetes control among patients presenting with acute myocardial infarction in a Canadian tertiary health care setting.
Bibliographic record
Abstract
BACKGROUND: The use of an insulin infusion following myocardial infarction, and its consequent lowering of glucose, significantly improves mortality. OBJECTIVE: To explore whether this knowledge was being applied in clinical practice. METHODS: The authors conducted a chart audit of all subjects with an acute myocardial infarction admitted to the coronary care unit of a tertiary health care centre in Nova Scotia between January 1, 2000 and December 31, 2000. Information was obtained from a computerized database as well as individual chart review. Treatment of patients with and without diabetes was specifically compared. RESULTS: There were 447 total admissions. One hundred and twenty-eight had diabetes mellitus and the majority of these (93%) had type 2 diabetes. Most patients (54%) sustained a non-Q wave infarction and 77% had either thrombolytic therapy or cardiac catheterization. Mean glucose on admission and discharge was 11.32 mmol/L and 9.86 mmol/L, respectively. Diabetes was managed conservatively among 98%, and only 2% received insulin infusion therapy. Only 16.3% of diabetic patients had their glycated hemoglobin A1c checked (mean value 9.12%). In-hospital mortality was 21% among patients with diabetes and 10.4% among those without. CONCLUSIONS: The present study highlights the poor outcome of patients with diabetes presenting with acute myocardial infarction. Despite clinical trial evidence suggesting that use of insulin infusion would confer significant benefit in this population, it was markedly underused even in a setting where other evidence-based therapies were well prescribed. These findings raise questions about the need for national clinical practice guidelines incorporating the use of insulin infusion in standard care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".