Cancer associated thrombosis and mortality in patients with cancer stratified by khorana score risk levels
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Bibliographic record
Abstract
BACKGROUND: The Khorana score (KS) clinical algorithm is used to predict VTE risk in cancer patients. The study objective was to evaluate VTE and survival rates among patients newly diagnosed with cancer and stratified by KS in a real-world population. METHODS: DataMart database between 01/01/2012-09/30/2017 was used to identify adults with ≥ 1 hospitalization or ≥ 2 outpatient claims with a cancer diagnosis (index date). Only patients who were initiated on chemotherapy or radiation therapy were included. Patients were classified based on KS (KS = 0, 1, 2 or ≥ 3). Time-to-first VTE and survival were evaluated from the index date to the earliest among end of data availability or insurance coverage, death, or 12 months post-index using Kaplan-Meier (KM) analyses. RESULTS: A total of 2,488 (KS = 0); 2,125 (KS = 1), 1,074 (KS = 2), and 507 (KS ≥ 3) cancer patients were included. The 12-month KM rates of VTE were 3.1%, 5.4%, 7.9%, and 14.9% (associated median time to VTE of 2.7, 3.0, 1.4, and 1.7 months) among KS = 0, 1, 2, and ≥ 3 cohorts, respectively. Corresponding adjusted hazard ratios (95% CIs) relative to the KS = 0 cohort were 1.72 (1.25-2.38), 2.46 (1.73-3.50), and 4.99 (3.40-7.31) for the KS = 1, 2, and ≥ 3 cohorts, respectively (all P < .001). Regardless of KS, patients with VTE had significantly lower survival rates than those without. CONCLUSIONS: This real-world claims-based cohort study of newly diagnosed cancer patients showed significantly higher rates of VTE with increased KS, confirming its predictive ability. Moreover, VTE was associated with lower survival rates within each KS cohort.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it