The Relationship Between Glycosylated Hemoglobin and Myocardial Perfusion Imaging
Bibliographic record
Abstract
BACKGROUND: The relationship between long-term glucose control (measured by glycosylated hemoglobin [HgbA1C]) and myocardial perfusion imaging (MPI) abnormalities in symptomatic diabetic patients has not been studied. HYPOTHESIS: We hypothesized that diabetic patients with poorly controlled HgbA1C would have more abnormal MPI compared to both patients without diabetes and diabetic patients with tighter glycemic control. METHODS: This was a retrospective evaluation of 1037 consecutive patients referred for MPI. All patients completed a 1-day MPI protocol. The electronic medical records were accessed for demographics and relevant medical history. RESULTS: Diabetic patients had a higher risk of abnormal MPI (including ischemia, infarction, and mixed ischemia/infarction) compared to nondiabetic patients (relative risk [RR] = 1.77). The populations with suboptimal (HgbA1C ≥ 7%) and poor (HgbA1C ≥ 8%) glycemic control had significantly higher risk of abnormal MPI (RR = 1.78 and 2.17, respectively) compared to nondiabetic patients. Coronary angiography supported the MPI results; 66% of diabetic patients had coronary artery disease (CAD), which was higher than the 53% of patients without diabetes found to have CAD. CONCLUSIONS: The importance of strict glycemic control to reduce cardiovascular complications in diabetic patients is well known. Our study shows a significantly higher risk of abnormal MPI and CAD in diabetic patients with suboptimal and poor long-term glycemic control, further emphasizing the need for aggressive risk factor modification to minimize vascular complications from DM.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".