The risk of venous thromboembolism is increased throughout the course of malignant glioma
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) frequently complicates the course of patients with cancer, and there is evidence to suggest that patients with brain tumors are at particularly high risk. The objective of this methodology-based literature review was to quantify the rate of incidence of VTE in patients with malignant glioma and to determine the factors that predict an increased risk of this complication. METHODS: Studies meeting predefined inclusion criteria were evaluated independently on an eight-item methodology index by three raters. Authors were contacted to resolve ambiguities. The results of the studies were summarized and the incidence rate of VTE within the early postoperative phase and during extended follow-up were reported separately. RESULTS: Within 6 weeks after surgery the incidence rate of deep venous thrombosis (DVT) ranged from 3% to 60%, varying with the prophylaxis regimen used, the method of diagnosis, and the study design. Beyond 6 weeks postoperatively, the rates of DVT ranged from 0.013 to 0.023 per patient-month of follow-up. The single study with no significant methodologic deficiencies found a 24% rate of incidence of symptomatic DVT over the 17 months of follow-up beyond the first 6 postoperative weeks. In 6 studies the presence of leg paresis, histologic diagnosis of glioblastoma multiform, age >/= 60 years, large tumor size, use of chemotherapy, and length of surgery > 4 hours were identified as possible risk factors. CONCLUSIONS: The incidence of VTE is high throughout the course of malignant glioma. A randomized, controlled trial is needed to clarify whether the benefits of long term anticoagulant prophylaxis outweigh the risks and costs of such therapy.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".