Use of rituximab in idiopathic retroperitoneal fibrosis
Bibliographic record
Abstract
BACKGROUND: Retroperitoneal fibrosis (RPF) is characterized by the proliferation of fibrous tissue in the retroperitoneum. The majority of RPF cases are due to idiopathic or IgG4-related disease. Recent studies on IgG4-related disease have shown rituximab to be an effective treatment. The current first-line treatment for idiopathic RPF (iRPF) is glucocorticoid therapy. Relapse rates vary widely in the literature, and DMARDs remain poorly studied. We sought to evaluate the efficacy of rituximab in idiopathic RPF by quantifying changes in iRPF diameter on imaging pre- and post-rituximab therapy and response by lab parameters in 10 iRPF patients. METHODS: We selected 10 patients diagnosed with iRPF and previously treated with rituximab (1000 mg) in two doses approximately 2 weeks apart. Pre- and post-therapy contrast enhanced cross-sectional abdomen and pelvis imaging were compared. In all patients, the thickest portion of the peri-aortic disease was measured in the axial and coronal planes. The presence of acute or long standing back pressure related renal findings were documented. Details of clinical visits including patient demographics and laboratory evaluations were collected pre- and post-therapy. Statistical analysis was performed using a Wilcoxon signed rank test. RESULTS: = 0.02). CONCLUSION: A comparison of pre and post-rituximab imaging studies revealed a statistically significant decrease in iRPF diameter following treatment with rituximab.
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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 | 0.001 | 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 | 0.000 | 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".