Multiple Intracranial Inflammatory Pseudotumors
Bibliographic record
Abstract
Introduction: Inflammatory pseudotumor is a rare intracranial lesion. Reports of multiple intracranial inflammatory pseudotumors are very rare. Case Report: A 68 year old woman presented with left hemiparesis and sensory neglect and was found to have two homogeneously enhancing lesions on computed tomography (CT) scan near the right rolandic area. The larger of the two lesions was resected with the presumed radiographic and intraoperative diagnosis of meningioma. Pathology demonstrated inflammatory pseudotumor. She re-presented 8 months postoperatively with both progression of the remaining lesion and a de novo lesion. Biopsy was done which reaffirmed the diagnosis. The patient was tried on high dose steroid, giving radiographic but not clinical improvement. Treatment was intensified to methotrexate which gave both clinical and radiographic improvement. The patient was then treated with Methotrexate giving symptomatic improvement. Conclusion: The differential for extra-axial lesions should include inflammatory pseudotumor. The best treatment for this disease process has not been determined. Surgical resection and steroid therapy may help temporize symptoms and provide a diagnosis but in the case of multiple lesions this likely represents a diffuse intracranial process requiring chemotherapy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".