Predictors of clinically relevant bleeding during extended anticoagulation for cancer-associated venous thromboembolism, including by cancer site: Post-hoc analysis of the API-CAT randomised trial
Bibliographic record
Abstract
Abstract Background : As shown by the API-CAT study, extended anticoagulation with reduced-dose apixaban is non-inferior to full-dose apixaban for preventing recurrent venous thromboembolism (VTE) in patients with active cancer, and is associated with fewer clinically relevant bleeding (CRB) and major bleeding (MB) complications. In this post-hoc analysis we sought to identify predictors associated with CRB. Methods : API-CAT was a randomised, double-blind, non-inferiority trial done in 121 hospitals in 11 countries. Consecutive patients (n=1,766) with active cancer and acute proximal deep vein thrombosis or pulmonary embolism who had completed at least 6 months of anticoagulation were randomised to receive apixaban 5.0 mg or 2.5 mg twice daily for 12 months. The key secondary outcome was CRB. Potential predictors of CRB were assessed in the overall population and according to cancer site (breast, prostate, lung, gynaecological, gastrointestinal, urogenital). In the intention-to-treat population, multivariable competing-risks regression model was built with CRB as the dependent variable and was adjusted for apixaban dose. The association between potential predictors and CRB was expressed as subhazard ratio (subHR) with 95% confidence interval. Variables with a p-value <0.05 were considered significantly associated with CRB and were subsequently evaluated as potential predictors of MB. Results : In the overall population, anaemia and/or thrombocytopenia, age ≥75, pulmonary embolism as the index event, and male sex were significantly associated with an increased risk of CRB. The same weight of predictors was found in the multivariable model for MB. These results appear homogeneous across cancer sites, with no evidence of interaction with dosing regimen. Conclusion : During extended treatment for cancer-associated VTE, four predictors of CRB were identified in the overall population. Three of these factors were also predictive for major bleeding, and across cancer sites, although the magnitude and significance of associations varied. There was no evidence of interaction with the dosing regimen.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".