Evidence of Cyclic Changes in the Metabolism of Abdominal Aortic Aneurysms During Growth Phases: <sup>18</sup>F-FDG PET Sequential Observational Study
Bibliographic record
Abstract
UNLABELLED: The rates of growth of medically treated abdominal aortic aneurysms (AAA) are difficult to determine, and relationships with parietal inflammation and with metabolic parameters from (18)F-FDG PET remain unclear. This (18)F-FDG PET sequential observational study was aimed at analyzing the metabolic changes accompanying the growth phases of medically treated AAA. METHODS: Thirty-nine patients (37 men; age [mean ± SD], 71 ± 12 y) exhibiting small and medically treated AAA (maximal diameter, 46 ± 3 mm) underwent (18)F-FDG PET and CT angiography at baseline and 9 mo later. Clinical and imaging parameter correlates of the 9-mo increase in maximal diameter were investigated; these included (18)F-FDG maximal standardized uptake values (SUVmax) averaged for slices encompassing the AAA volume. RESULTS: Of the 39 patients, 9 (23%) had a significant (≥2.5 mm) increase in maximal diameter at 9 mo, whereas the remaining 30 did not. The patients with an increase in maximal diameter at 9 mo exhibited lower SUVmax within the AAA at baseline than patients who did not have such an increase (1.80 ± 0.45 vs. 2.21 ± 0.52; P = 0.04); they also displayed a trend toward greater changes in SUVmax at 9 mo (difference between 9 mo and baseline: +0.40 ± 0.85 vs. -0.06 ± 0.57; P = 0.07). Similar levels were ultimately reached in both groups at 9 mo (2.20 ± 0.83 and 2.15 ± 0.66). SUVmax was a significant, yet modest, baseline predictor of the absolute change in maximal diameter during follow-up (P = 0.049). CONCLUSION: The enhancement in the maximal diameter of small AAA was preceded by a stage with a low level of (18)F-FDG uptake, but this low level of uptake was no longer documented after the growth phases, suggesting a pattern of cyclic metabolic changes.
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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 | 0.001 | 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".