Cerebellar cavernous malformation in pediatric patients: defining clinical, neuroimaging, and therapeutic characteristics
Bibliographic record
Abstract
OBJECT: Cerebellar cavernous malformations (CCMs) have not been specifically described in the pediatric age group. Authors of this study, after considering the published literature, describe the characteristic clinical, radiological, and surgical features of CCM in children. METHOSDS: Patients younger than 18 years of age who were known to have CCM and had undergone surgery between 1992 and 2014 at the authors' institution were reviewed. Pediatric CCM cases reported in the literature (case reports and cases included in series on CMs in the pediatric age group) were also analyzed for specific features of this entity. RESULTS: Four male patients and 1 female patient (2.5-14 years of age) with CCM presented acutely with severe headache followed by cerebellar dysfunction. In all patients, neuroimaging (cranial CT and MRI) demonstrated hemorrhagic cerebellar lesions with heterogeneous T1 and T2 signal intensities and hyperintense blooming on susceptibility-weighted imaging. The lesions reached large sizes exhibiting spherical, cystic, and often "pseudotumoral" morphology. In 3 patients, developmental venous anomalies (DVAs) were found. In 4 of the 5 patients, the CCMs and hematomas were totally removed. All patients had a clinically excellent functional outcome without surgical complication and with complete resolution of their presenting symptoms. CONCLUSIONS: Cerebellar CMs occur in all pediatric age groups and display characteristic clinical and imaging features. In children, CCMs reach large sizes and can result in massive hemorrhage, often leading to a possible diagnosis of hemorrhage into a tumor. An associated DVA is quite common. Surgery is a safe and efficient treatment option with excellent outcomes in patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".