Effect of Diet on Physiologic Bowel<sup>18</sup>F-FDG Uptake
Bibliographic record
Abstract
Intestinal 18F-FDG uptake is variable in whole-body PET/CT. In cancer patients, particularly those suspected of relapse or metastasis, 18F-FDG absorption might interfere with scan interpretation. This study evaluated the effect of diet on intestinal 18F-FDG absorption. Methods: In total, 214 patients referring for oncologic 18F-FDG PET/CT participated. They were randomly divided into 2 groups and asked to follow either a routine diet (RD) or a low-carbohydrate, high-fat diet (LCHFD) for 24 h before the study. The small bowel and different parts of the colon (the cecum; the ascending, transverse, and descending segments; and the hepatic and splenic flexures) were evaluated and visually interpreted by nuclear medicine experts. Bowel uptake was graded through comparison with that in the liver as absent, mild, moderate, or severe. Results: Significantly higher 18F-FDG uptake in the descending colon ( P = 0.001) and small intestine ( P = 0.01) was observed in the RD group than in the LCHFD group. After patients with bowel cancer were omitted from the statistical analysis, no significant differences in the final results were seen. Conclusion: An LCHFD for 24 h before 18F-FDG PET imaging resulted in lower 18F-FDG uptake in the descending colon and small bowel than did an RD, assisting the interpreting physician by reducing the intestinal activity interference for more accurate diagnostic interpretation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".