Risk of Venous Thromboembolism in Glioblastoma Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it