Anaplastic Meningioma Arising From a Radiologically Diagnosed Arachnoid Cyst
Bibliographic record
Abstract
OBJECTIVE AND IMPORTANCE: We report the first example of an anaplastic meningioma arising from an intracranial arachnoid cyst and discuss the diagnostic challenges of this case, including the useful role of genetic markers. CLINICAL PRESENTATION: A 72-year-old man presented with transient episodes of expressive dysphasia and focal motor seizures, superimposed on a 6-month history of worsening headaches and dizziness. His past history was significant for a previously drained left-sided chronic subdural hematoma and a radiologically diagnosed left middle fossa arachnoid cyst. Magnetic resonance imaging on admission showed variable wall thickening of the arachnoid cyst with mild mass effect on the left frontotemporal lobes. INTERVENTION: The patient underwent decompression of the arachnoid cyst and biopsy of the cyst wall. Histologic and immunohistochemical studies of the thickened portion initially suggested a metastatic carcinosarcoma, but fluorescence in situ hybridization (FISH) studies confirmed the diagnosis of anaplastic meningioma based on characteristic chromosomal deletions. The patient returned 2 months later with progressive disease, leading to his death 6 weeks later despite repeat surgery for tumor debulking. CONCLUSION: Malignant transformation of meningothelial elements in arachnoid cysts is an exceptionally rare complication that poses considerable diagnostic challenges. Genetic markers may be particularly helpful in such cases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".