Cerebellar glioblastoma: a clinicopathologic series of 16 cases
Bibliographic record
Abstract
Due to their rareness, it is not known if the clinicopathological features of cerebellar glioblastomas (cGBMs) are different from supratentorial GBMs (sGBMs). We reviewed all 16 cases of cGBMs (total GBMs: 1350) at St. Michael’s Hospital over 18 years and assessed their clinicopathologic features. The mean age at diagnosis was 57 years. The most common presentations were headache (56%) and gait instability (56%). The majority (81%) of cGBMs were hemispheric while 19% involved the midline. There was radiologic evidence of brainstem infiltration at presentation in one case. Radiologically, peritumoral edema (63%) and heterogeneous contrast enhancement (50%) were common. Histologically, cGBM showed leptomeningeal involvement in 10/12 of cases. Uncommon histologic variants included 3 giant cell GBMs, a gliosarcoma, and a tumor with Rosenthal fibres and eosinophilic granular bodies. IDH1 R132H mutation was detected in 3/14 cases, a rate much higher than sGBMs. Additionally, 7/11 tumors had widespread p53 immunopositivity suggestive of TP53 mutation which is in accordance with previous reports in the literature. Of 9 cases tested, none had histone H3 K27M or G34R/V mutation. In summary, cGBMs have unique features that distinguishes them from sGBMs. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Identify the clinicopathological features of cerebellar GBMs including major molecular alterations 2. Compare cerebellar and supratentorial GBMs and describe the distinguishing features of each type of tumor
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".