Incidence of Venous Thromboembolism in Newly Diagnosed Glioblastoma and Associated Risk Factors: A Retrospective Chart Review
Bibliographic record
Abstract
This was a single-centre retrospective cohort study of patients diagnosed with glioblastoma (GB) at the Juravinski Cancer Centre (JCC). The charts of 528 patients diagnosed with GB at the JCC from an 8-year period from 1 January 2013, to 31 December 2020, were reviewed. The primary objective was to assess the incidence of venous thromboembolism (VTE) in newly diagnosed GB. The secondary objective was to identify patients at higher risk of developing VTE to understand who might benefit from prophylactic anticoagulation. Data on the following factors were collected: date of diagnosis, time to death or last follow-up, location and size of tumour, degree of resection, presence and location of weakness, performance status, body mass index, comorbidities (hypertension, diabetes, dyslipidemia, smoking history), baseline blood counts, and treatments administered. A total of 111 of the 528 patients (21%) were diagnosed with VTE. Most VTE (87%) occurred within 12 months of diagnosis. A previous cancer diagnosis and recurrence or disease progression were the only factors identified as predictive of a higher risk for developing thrombosis. Newly diagnosed patients with GB have been shown to have a significant risk of developing VTE. Consideration should be given for prophylactic anticoagulation at the time of diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".