Pancreatic size and fat content in diabetes: A systematic review and meta-analysis of imaging studies
Bibliographic record
Abstract
OBJECTIVES: Imaging studies are expected to produce reliable information regarding the size and fat content of the pancreas. However, the available studies have produced inconclusive results. The aim of this study was to perform a systematic review and meta-analysis of imaging studies assessing pancreas size and fat content in patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM). METHODS: Medline and Embase databases were performed. Studies evaluating pancreatic size (diameter, area or volume) and/or fat content by ultrasound, computed tomography, or magnetic resonance imaging in patients with T1DM and/or T2DM as compared to healthy controls were selected. Seventeen studies including 3,403 subjects (284 T1DM patients, 1,139 T2DM patients, and 1,980 control subjects) were selected for meta-analyses. Pancreas diameter, area, volume, density, and fat percentage were evaluated. RESULTS: Pancreatic volume was reduced in T1DM and T2DM vs. controls (T1DM vs. controls: -38.72 cm3, 95%CI: -52.25 to -25.19, I2 = 70.2%, p for heterogeneity = 0.018; and T2DM vs. controls: -12.18 cm3, 95%CI: -19.1 to -5.25, I2 = 79.3%, p for heterogeneity = 0.001). Fat content was higher in T2DM vs. controls (+2.73%, 95%CI 0.55 to 4.91, I2 = 82.0%, p for heterogeneity<0.001). CONCLUSIONS: Individuals with T1DM and T2DM have reduced pancreas size in comparison with control subjects. Patients with T2DM have increased pancreatic fat content.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".