P.126 An unlikely impersonator of primary brain tumours: Illustrative case report and literature review of primary angiitis of the central nervous system
Bibliographic record
Abstract
Background: Primary angiitis of the central nervous system (PACNS) is a rare inflammatory condition affecting the parenchymal and leptomeningeal vessels of the CNS. PACNS presenting as a solitary mass lesion (ML-PACNS) constitutes a rare subtype of this pathology. Herein we present the first case reported in Canada of ML-PACNS, presenting with clinical and radiographic findings consistent with a high grade glial neoplasm, as well as a review of the literature on ML-PACNS. Methods: Review of the literature from 1987-2023 was conducted using PubMed to identify features of ML-PACNS and possible treatment paradigms. Results: A number of case reports of ML-PACNS were identified, as well as 6 retrospective analyses of a total of 67 patients. Features such as faster rate of symptom onset, and investigations such as MRI vessel-wall imaging and MR spectroscopy were suggested for identification of ML-PACNS. Treatment was highly variable, but followed guidelines for other neuroinflammatory disorders. Conclusions: Preoperative differentiation between ML-PACNS and CNS neoplasms is difficult due to their similar clinical and radiographic features. However, making this distinction is crucial as PACNS mass lesions can regress entirely with immunosuppressive therapy, potentially obviating the requirement for surgical intervention. Beyond diagnostics, further research is required to establish and validate a treatment paradigm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".