P.167 Documented growth of a de novo intracranial capillary hemangioma: a case report
Bibliographic record
Abstract
Background: Intracranial capillary hemangiomas are rare, particularly in adults, and diagnosis can be challenging. The literature lacks visualization of intracranial capillary hemangioma growth over time. Here we document growth of a de novo intracranial capillary hemangioma, initially interpreted radiologically as a glioma. Methods: We report a case of a 64 year old male with history of HIV, recent Lyme disease and unconfirmed prior COVID-19 infection, who presented with exhaustion and confusion. Imaging demonstrated an intra-axial high T2/FLAIR signal lesion centred in the subcortical white matter of the posterior right temporal lobe. There was faint enhancement, and a few mildly prominent vessels were seen along its anterior aspect. Imaging 2 years prior had not shown the lesion. Stereotactic biopsy was nondiagnostic. Craniotomy and resection was carried out. Results: Pathological examination and immunohistochemistry returned the diagnosis of capillary hemangioma. We review how this case adds to proposed theories of de novo intracranial capillary hemangioma growth. Our patient’s co-morbidities support possible inflammation related triggers for symptomatic progression of these uncommon lesions. Conclusions: This unusual case documents the radiological appearance and progression of a de novo intracranial capillary hemangioma. It represents the first time such growth has been visualized in an adult male.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".