Chronic Vascular Graft Infection With Fistula to Bone Causing Vertebral Osteomyelitis, Imaged With F-18 FDG PET/CT
Bibliographic record
Abstract
Vascular grafts have an infection rate ranging from 1% to 3%. While early infections occur within 4 months after surgery and are associated with virulent organisms, late infections can occur after months to years of surgery and are often caused by low virulence organisms that survive in an adherent biofilm. Host defense recognition of bacterial biofilm can result in perigraft abscesses, aorto-enteric fistulas, and very rarely, fistulas into adjacent bone. We present a case of an 83-year-old man, who had an F-18 FDG PET/CT scan for workup of a solitary pulmonary nodule, and was incidentally diagnosed with chronic multifocal infection of an aorto-iliac vascular graft, with an infected fistula tract into adjacent bone causing chronic vertebral osteomyelitis, which was confirmed with a contrast-enhanced CT. The patient was asymptomatic and not a surgical candidate, and was treated conservatively with a course of antibiotics. This case highlights the utility of F-18 FDG PET/CT in the imaging of chronically infected vascular grafts and in identifying potentially lethal complications such as fistulas into adjacent structures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".