Incidence of venous thromboembolism in newly diagnosed glioblastoma multiforme and associated risk factors: A retrospective chart review.
Bibliographic record
Abstract
e14040 Background: Patients diagnosed with glioblastoma multiforme (GBM) are recognized as a population at high risk of developing venous thromboembolism (VTE) (1,2). A retrospective chart review of patients diagnosed with GBM at the Juravinski Cancer Centre (JCC) was performed with the primary objective to assess the incidence of the development of VTE in newly diagnosed GBM. The secondary objective was to identify patients who were at higher risk and who could benefit from prophylactic anticoagulation. Methods: This was a single-centre, retrospective cohort study. We reviewed the charts of 528 patients diagnosed with GBM at the JCC from an 8-year period from January 1, 2013 to December 31, 2020. Information on the following factors was collected: Body-mass index (BMI), comorbidities (hypertension, diabetes, dyslipidemia, smoking history), performance status, location and size of tumour, degree of resection, presence of and location of weakness, baseline blood counts, treatments administered, date of diagnosis and time to death or last follow-up. Results: Over the period of review, a total of 528 patients were identified. 111 patients (21%) were diagnosed with VTE. The location of VTE was as follows: 39 patients (35%) unilateral lower extremity deep venous thrombosis (DVT), 8 (7%) unilateral upper extremity DVT, 8 (7%) bilateral lower extremity DVT, 30 (27%) pulmonary embolism (PE), and 25 (23%) DVT and PE. Most VTE (87%) occurred within 12 months of diagnosis of GBM. On univariate and multivariate analysis, no factors were identified that contributed to an increased risk of VTE. Conclusions: Newly diagnosed patients with GBM have been shown to have a significant risk of developing VTE. The AVERT study (3) demonstrated the preventive benefit of administering a direct oral anticoagulant (DOAC) to cancer patients, including those with GBM, at high risk of developing VTE (3). Thus consideration should be given to treating patients with GBM with a DOAC at the time of diagnosis. The benefit of preventive treatment will need to be balanced against the risk of bleeding in these patients. References: 1. Marras LC et al. Cancer 2000; 89(3):640–6; 2. Lim G et al. Cureus 2018;10(5): e2678; 3. Carrier M et al. N Engl J Med 2019; 380:711-9.
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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) | 0.000 | 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".