Risk of Venous Thromboembolism in Glioblastoma Patients
Bibliographic record
Abstract
Background Patients with cancer are at increased risk of venous thromboembolic events (VTE) with a particularly high prevalence in patients with glioblastoma (GB). We designed this current study to determine the incidence of symptomatic VTE in patients with GB undergoing first-line chemoradiotherapy and to develop a clinical score to help physicians identify those who are at the highest risk of VTE. Methods A retrospective study cohort included patients diagnosed with GBM treated with radical concurrent chemoradiotherapy between 2005 and 2010 in Southern Alberta. Descriptive statistics were used to characterize the patient population. A predictive value for VTE was assessed by comparing logistic models and using the area under the receiver operating characteristic curve. Results Twenty-three out of 115 patients (20%) experienced a symptomatic VTE. This complication was not associated with overall survival at two years (p=0.06, heart rate (HR)=1.61). Hypertension and smoking were associated with VTE (p-values 0.034 and 0.048, respectively). A scoring system with the following variables was developed to predict the likelihood of developing VTE: (1) Karnofsky performance status (KPS) - 70, 1 point; KPS < 70, 2 points; (2) Age - 45 to 60, 1 point; 61 to 70, 2 points; (3) Current smoking, 1 point; (4) Hypertension, 1 point. Patients with >3 points were 5 times more likely to develop a VTE. Conclusions In our population, our simple scoring system allows the identification of patients with GB receiving first-line therapy, who are at the highest risk of VTE. These results require validation in an independent series.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".