Retrospective Cohort Study of Venous Thromboembolism Rates in Ambulatory Cancer Patients: Association With Khorana Score and Other Risk Factors
Bibliographic record
Abstract
BACKGROUND: Guidelines do not recommend that cancer outpatients receive thromboprophylaxis unless at high venous thromboembolism (VTE) risk, with the Khorana score suggested for risk stratification. This study investigated VTE incidence in outpatients with pancreatic, endometrial, colorectal, ovarian and cervical cancer, the role of Khorana score in risk assessment and potential risk factors. METHODS: Data were retrospectively collected 1 year after cancer diagnosis. VTE associated with inpatient admissions was excluded. RESULTS: Seven hundred forty-six patients were included. VTE rates varied: 26.8% pancreatic; 5.7% endometrial; 9.8% colorectal; 10.2% ovarian; and 0.0% cervical cancer. Excluding VTE at diagnosis, potentially preventable VTE rates were 16.5% in pancreatic, 3.8% in endometrial, 9.8% in colorectal and 8.7% in ovarian cancer. Khorana score was associated with VTE in endometrial cancer only (high-risk: 16.7% vs. low-risk: 1.5%; P < 0.001). VTE rates for patients with central venous catheters (CVCs) were 22.6-34.8% in pancreatic, endometrial, colorectal and ovarian cancers. VTE was associated with CVCs in endometrial, colorectal and ovarian; chemotherapy and Hb < 100 g/L in pancreatic; surgery in endometrial and ovarian; and body mass index > 35 in ovarian cancers following adjusted analysis (P < 0.05). CONCLUSIONS: VTE is a significant burden in pancreatic, endometrial, colorectal and ovarian cancers. Khorana score was not predictive in most cancers. The major VTE-associated variable was CVC. Our data suggest a role for clinical trials of thromboprophylaxis in targeted cancer outpatients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".