Prognostic Factors for Venous Thromboembolism in Patients with Solid Tumours on Systemic Therapy: A Systematic Review
Bibliographic record
Abstract
Abstract Background Patients undergoing systemic cancer therapy are susceptible to developing venous thromboembolism (VTE). The most pertinent prognostic factors for VTE remain unclear. This systematic review aims to summarize prognostic factors associated with VTE in this population. Methods MEDLINE, Embase, and CENTRAL databases were searched for observational or randomized studies that used multivariable analysis adjusted for tumor type and/or metastatic disease to model the risk of VTE. Adjusted effect estimates for each prognostic factor were collected for all of the included studies. Risk of bias was assessed using the Quality in Prognostic Factor Studies (QUIPS) tool. Results From 5,988 search results, 15 eligible studies and 42 prognostic factors were identified. A total of 8,554 patients of whom 456 (5.33%) developed VTE were included. Fourteen studies had a high risk of bias and one study had a moderate risk. The most commonly reported prognostic factors include age, gender, tumor site, metastasis, performance status, and systemic therapy type. Poor performance status and the use of platinum-based chemotherapy compounds were associated with an increased risk of VTE across the majority of studies. The evidence to suggest that the other prognostic factors identified were associated with VTE development was inconclusive. Several individual studies identified novel biomarkers for VTE. Heterogeneity in statistical methods and prognostic factor definitions across studies precluded meta-analysis. Conclusion Overall, many prognostic factors were identified; however, the evidence for association with development of VTE for most of the factors is inconclusive. Findings were limited by high heterogeneity and risk of bias in the included studies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".