Uncommon Primary Pelvic Retroperitoneal Masses in Adults: A Pattern-based Imaging Approach
Bibliographic record
Abstract
There is a broad spectrum of primary pelvic retroperitoneal masses in adults that demonstrate characteristic epidemiologic and histopathologic features and natural histories. These masses may be classified into five distinct subgroups using a pattern-based approach that takes anatomic distribution and certain imaging characteristics into account, allowing greater accuracy in their detection and characterization and helping to optimize patient management. The five groups are cystic (serous and mucinous epithelial neoplasms, pelvic lymphangioma, tailgut cyst, ancient schwannoma), vascular or hypervascular (solitary fibrous tumor, paraganglioma, pelvic arteriovenous malformation, Klippel-Trénaunay-Weber syndrome, extraintestinal GIST [gastrointestinal stromal tumor]), fat-containing (lipoma, liposarcoma, myelolipoma, presacral teratoma), calcified (calcified lymphocele, calcified rejected transplant kidney, rare sarcomas), and myxoid (schwannoma, plexiform neurofibroma, myxoma).Cross-sectional imaging modalities help differentiate the more common gynecologic neoplasms from more unusual masses. In particular, the tissue-specific multiplanar capability of high-resolution magnetic resonance imaging permits better tumor localization and internal characterization, thereby serving as a road map for surgery.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".