Evaluation of the Khorana score for prediction of venous thromboembolism in patients with multiple myeloma
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend thromboprophylaxis for patients with multiple myeloma (MM) at high risk for venous thromboembolism (VTE). However, the optimal risk prediction model for VTE in MM remains unclear. Khorana et al developed a VTE risk score (Khorana score) in ambulatory cancer patients receiving chemotherapy. We aimed to evaluate the predictive ability of the Khorana score in patients with MM. METHODS: We identified patients with MM within the Veterans Affairs health care system between 2006 and 2013. The Khorana score was calculated before treatment initiation. Using logistic regression, the relationship between risk group and VTE was assessed at 3 and 6 months. We tested model discrimination using the concordance statistic. RESULTS: In the cohort of 2870 patients with MM, there were 1328 at low risk (0 points), 1521 at intermediate risk (1-2 points), and 21 at high risk (≥3 points) for VTE by the Khorana score. The 6-month cumulative incidence of VTE was 5.1% (95% confidence interval [CI], 4.0%-6.4%) in low risk, 3.9% (95% CI, 3.0%-5.0%) in intermediate risk, 4.8% (95% CI, 0.3%-20.2%) in high risk. The Khorana score did not strongly discriminate between patients who did and did not develop VTEs at 3 or 6 months (concordance statistic, 0.58; 95% CI, 0.54-0.63; and 0.53, 95% CI, 0.50-0.57, respectively. CONCLUSIONS: In conclusion, in this cohort of 2870 patients with MM, the Khorana score did not predict VTE. Our study supports the need to use myeloma-specific risk models to predict VTE risk in patients with MM.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".