Intravenous administration of diazepam significantly reduces brown fat activity on 18F-FDG PET/CT.
Bibliographic record
Abstract
BACKGROUND: Brown adipose tissue (BAT) activity on (18)F-fluorodeoxyglucose (FDG) PET/CT can introduce an undesirable element of complexity when attempting to discern physiologic activity from more ominous entities. Recent studies have demonstrated several methods to reduce BAT FDG uptake. Benzodiazepines, however, have yet to been proven effective against BAT. METHODS: Twenty-five patients with increased BAT FDG uptake were selected retrospectively from our PET/CT database between November 2004 and January 2011. These patients had been asked to return on a different day for repeat scanning with 5mg of intravenous diazepam, administered ten minutes prior to FDG. Two patients underwent this procedure on a second occasion (for a follow-up scan at a later date), thus resulting in a total of twenty-seven scans from twenty five patients. FDG uptake in BAT was recorded using the maximum standardized uptake value (SUVmax). RESULTS: The mean basal BAT SUVmax was 10.1 ± 4.6 compared to a mean SUVmax of 2.8 ± 3.3 post IV diazepam (p < 0.0001). Approximately 89% (24 of 27) of scans had no significant residual BAT activity. The three remaining scans had a reduction in SUVmax ranging from 23-64% following diazepam administration. No adverse effects were noted. CONCLUSION: We observed a significant reduction in brown fat activity in para-spinal, cervical, mediastinal, para-adrenal, and supra- and infra-clavicular regions on PET/CT following premedication with intravenous diazepam. We feel that IV benzodiazepines should be considered a pharmacologic option for reducing BAT FDG uptake, which in turn, will aid in distinguishing physiologic metabolic activity from pathology.
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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 | 0.000 | 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) | 0.000 | 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 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".