The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
We aimed to evaluate the performance of the Khorana score in predicting venous thromboembolic events in ambulatory cancer patients. Embase and MEDLINE were searched from January 2008 to June 2018 for studies which evaluated the Khorana score. Two authors independently screened studies for eligibility, extracted data, and assessed risk of bias. Additional data on the 6-month incidence of venous thromboembolism were sought by contacting corresponding authors. The incidence in each Khorana score risk group was estimated with random effects meta-analysis. A total of 45 articles and eight abstracts were included, comprising 55 cohorts enrolling 34,555 ambulatory cancer patients. For 27,849 patients (81%), 6-month follow-up data were obtained. Overall, 19% of patients had a Khorana score of 0 points, 64% a score of 1 or 2 points, and 17% a score of 3 or more points. The incidence of venous thromboembolism in the first six months was 5.0% (95%CI: 3.9-6.5) in patients with a low-risk Khorana score (0 points), 6.6% (95%CI: 5.6-7.7) in those with an intermediate-risk Khorana score (1 or 2 points), and 11.0% (95%CI: 8.8-13.8) in those with a high-risk Khorana score (3 points or higher). Of the patients with venous thromboembolism in the first six months, 23.4% (95%CI: 18.4-29.4) had been classified as high risk according to the Khorana score. In conclusion, the Khorana score can be used to select ambulatory cancer patients at high risk of venous thromboembolism for thromboprophylaxis; however, most events occur outside this high-risk group.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".