Expanding the imaging armamentarium for the diagnosis of prosthetic aortic graft infection
Bibliographic record
Abstract
In their study published in this issue of the European Journal of Cardio-Thoracic Surgery, Tsuda et al. [1] evaluated the role of 18-fluorodeoxyglucose positron emission tomography (18-F-FDG PET) in the diagnosis of prosthetic aortic graft infection. Their retrospective review of 71 patients who underwent 18-FDG PET imaging after aortic repair showed that patients with aortic graft infection had a higher maximum standardized uptake value (SUVmax) than patients without infection (8.64 vs 3.40, P < 0.01) and that SUVmax value differed between the early postoperative period and long-term follow-up. The authors propose a cut-off value for SUVmax of 4.5 for suspicion of graft infection immediately after endovascular repair and subsequently using stepwise cuff-off values of 6.5 at 1 year, 5.5 at 3 years and 5.0 thereafter. Aortic graft infection is an uncommon complication, and its diagnosis and treatment can be challenging. The recent American Heart Association aortic disease guideline [2] provides recommendations for the diagnosis and management of prosthetic aortic graft infection. The diagnostic recommendations state that the sensitivity and specificity of diagnosis are enhanced by the use of cross-sectional imaging with computed tomography angiography combined with 18-FDG PET or magnetic resonance imaging of tagged white blood cells. Furthermore, the Management of Aortic Graft Infection Collaboration (MAGIC) group has proposed clinical/surgical, radiological, and laboratory diagnostic criteria for aortic graft infection, among which suspicious metabolic activity on 18-FDG PET is a minor criterion [3]. The MAGIC criteria were shown to have with good sensitivity and specificity in the prospective Vascular Graft Cohort study (VASGRA) [3]. Importantly, FDG-PET interpretation can be limited in patients with natural inflammation or malignancy that can affect its diagnostic accuracy for infection, as well as differences among centres and practitioners in their methods of image acquisition, processing and interpretation [4].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".