Venous Thromboembolism in Patients with Sarcoma: A Retrospective Study
Bibliographic record
Abstract
Abstract Background Little has been published about the association of venous thromboembolism (VTE) and sarcoma. In this study, we sought to identify clinical features of patients with sarcoma presenting at least one VTE episode. Methods Our study was a retrospective case–control study of a single-institution database with univariate and multivariate analysis using chi-square and Student's t test. A p value less than .05 was considered significant. Results The overall incidence of VTE in patients with sarcoma was 7.9%. Predictive factors identified by multivariate analysis were metastatic disease and administration of chemotherapy. It was not statistically possible to correlate the risk of VTE with specific sarcoma subtypes, but observations suggested malignant peripheral nerve sheath tumor, osteosarcoma, and liposarcoma as having the highest propension. Conclusion VTE is not infrequent in patients with sarcoma. Adoption of common guidelines for cancer-associated thrombosis is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".