Giant Cell Glioblastoma: Predictive and Prognostic Factors
Bibliographic record
Abstract
Objective: To evaluate predictive and prognostic factors in giant cell glioblastomas. Methods: WHO grade III astrocytomas with significant giant cell elements were studied in a tissue microarray (48 tumor biopsies from 37 individuals) by immunohistochemistry and FISH for 1p and 19q deletions using the VYSIS 1p/19q probe set. LOH 1p and 19q microsatellite analysis was also undertaken on 12/37. Survival, giant cell and oligodendroglioma component were evaluated. Results: Oligodendroglial elements, identified in all cases, were significant in 22/37 (59.5%). Of the 27/34 with poor (<12 months) or intermediate (1–2 years) survival, 3 had 19‐, 4 were polyploid, and 1 showed distal LOH 1p. In 1/7 showing better outcome (survival >2 years), small interstitial 1p deletions were found in 2 samples from the same case. Clear LOH 1p was not demonstrated in any case. Predominance of giant cell elements (5/7) and relatively distinct margins (2/7) were also associated with longer survival (>24 months). In 2/7, giant cell elements were identified after a diagnosis of oligodendroglioma in a previous biopsy. Conclusions : Giant cell glioblastoma comprises a heterogeneous group that encompasses high‐grade gliomas associated with very aggressive biologic behavior, to a smaller group associated with a better prognosis. The lack of significant chromosomal loss of 1p within the favorable group implicates other genetic variables.
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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 | 0.000 |
| 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".