The Role of EGFR Amplification in Deep Venous Thrombosis Occurrence in IDH Wild-Type Glioblastoma
Bibliographic record
Abstract
Introduction: Glioblastoma (GBM) patients have a 20–30 incidence of venous thromboembolic events. EGFR is a widely used prognostic marker for many cancers. Recent lung cancer studies have described relationships between EGFR amplification and an increased incidence of thromboembolic complications. We aim to explore this relationship in glioblastoma patients. Methods: Two hundred ninety-three consecutive patients with IDH wild-type GBM were included in the analysis. The amplification status of EGFR was measured using fluorescence in situ hybridization (FISH). Centromere 7 (CEP7) expression was recorded to calculate the EGFR-to-CEP7 ratio. All data were collected retrospectively through chart review. Molecular data were obtained through the surgical pathology report at the time of biopsy. Results: There were 112 subjects who were EGFR-amplified (38.2%) and 181 who were non-amplified (61.8%). EGFR amplification status was not significantly correlated with VTE risk overall (p = 0.2001). There was no statistically significant association between VTE and EGFR status after controlling for Bevacizumab therapy (p = 0.1626). EGFR non-amplified status was associated with an increased VTE risk in subjects greater than 60 years of age (p = 0.048). Conclusions: There was no significant difference in occurrence of VTE in patients with glioblastoma, regardless of EGFR amplification status. Patients older than 60 years of age with EGFR amplification experienced a lower rate of VTE, contrary to some reports on non-small-cell lung cancer linking EGFR amplification to VTE risk.
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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) | 0.002 | 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".