Retroperitoneal Fasciitis: Spectrum of CT Findings in the Abdomen and Pelvis
Bibliographic record
Abstract
Retroperitoneal fasciitis is a rare but potentially lethal complication of infection. Early diagnosis is crucial and is usually made when there is a high degree of clinical suspicion combined with characteristic imaging findings leading to early surgical intervention. Computed tomography (CT) can play a central role in demonstrating early findings, assessing the extent of disease to help determine the best surgical approach, identifying the primary source of infection, and evaluating the treatment response. The possible presence of retroperitoneal fasciitis should be considered in patients presenting with symptoms of sepsis, including pain that is disproportionate with the clinical abnormality. When retroperitoneal fasciitis is suspected, emergency CT can facilitate early diagnosis and evaluation of the extent of disease. Common findings at CT include fascial thickening and enhancement, muscular edema, fat stranding, fluid collections, and abscess formation. Gas tracking along fascial planes in the retroperitoneum is the hallmark of retroperitoneal fasciitis but is not seen in all cases. Another important clue to the diagnosis is asymmetric involvement of the retroperitoneal fascial planes and deep tissues. Fasciitis in the retroperitoneum may originate from infected retroperitoneal organs or from infection that spreads along indirect and/or direct pathways from a primary source elsewhere in the body. Findings of indirect tracking and transgression of fascial planes may indicate more severe infection associated with the necrotizing form of retroperitoneal fasciitis. Despite aggressive antibiotic treatment, early and repeated surgical débridement may be required to remove nonviable tissue in patients with the necrotizing form of retroperitoneal fasciitis. Awareness of the anatomy of the retroperitoneum, potential routes of spread of infection, and the spectrum of CT findings in retroperitoneal fasciitis is needed to achieve prompt diagnosis and guide treatment.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".