Pancreatic 18F-FDG uptake is increased in type 2 diabetes patients compared to non-diabetic controls
Bibliographic record
Abstract
INTRODUCTION: Increasing evidence indicates that the development of type 2 diabetes is driven by chronic low grade beta-cell inflammation. However, it is unclear whether pancreatic inflammation can be noninvasively visualized in type 2 diabetes patients. We aimed to assess pancreatic 18F-FDG uptake in type 2 diabetes patients and controls using 18F-fluorodeoxylglucose positron emission tomography/computed tomography (18F-FDG PET/CT). MATERIAL AND METHODS: In this retrospective cross-sectional study, we enrolled 20 type 2 diabetes patients and 65 controls who had undergone a diagnostic 18F-FDG PET/CT scan and obtained standardized uptake values (SUVs) of pancreas and muscle. Pancreatic SUV was adjusted for background uptake in muscle and for fasting blood glucose concentrations. RESULTS: The maximum pancreatic SUVs adjusted for background muscle uptake (SUVmax.m) and fasting blood glucose concentration (SUVglucose) were significantly higher in diabetes patients compared to controls (median 2.86 [IQR 2.24-4.36] compared to 2.15 [IQR 1.51-2.83], p = 0.006 and median 2.76 [IQR 1.18-4.34] compared to 1.91 [IQR 1.27-2.55], p<0.001, respectively). In linear regression adjusting for age and body mass index, diabetes remained the main predictor of SUVmax.m and SUVglucose. CONCLUSION: Pancreatic 18F-FDG uptake adjusted for background muscle uptake and fasting blood glucose concentration was significantly increased in type 2 diabetes 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".