The Relationship Between Glycosylated Hemoglobin and Myocardial Perfusion Imaging
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it