Growth differentiation factor‐15 for prediction of bleeding in cancer patients
Bibliographic record
Abstract
BACKGROUND: Growth differentiation factor-15 (GDF-15) is a strong predictor for bleeding in patients with atrial fibrillation, but there are no data on cardiovascular outcomes for this biomarker in cancer patients. Bleeding risk assessment is important in cancer patients when considering primary thromboprophylaxis because it is associated with an increased bleeding risk. OBJECTIVES: To evaluate GDF-15 as predictor for bleeding events in cancer patients previously enrolled in the AVERT trial. PATIENTS/METHODS: In this trial, 574 participants were randomized to prophylactic apixaban or placebo and followed for 180 days for venous thromboembolism, major bleeding, clinically relevant nonmajor bleeding, and any bleeding. Plasma concentrations of GDF-15 were measured centrally with the Elecsys GDF-15 commercial assay kit (Roche Diagnostics GmbH). RESULTS: In apixaban recipients, the area under the receiver operator characteristic curve of GDF-15 for major bleeding was 0.73 (95% confidence interval [CI], 0.44-1.00). Compared with the lowest GDF-15 tertile (<1470 ng/L), major bleeding risk was significantly higher in the highest tertile (≥2607 ng/L; hazard ratio [HR] 3.19; 95% CI, 2.41-4.22), also when adjusting for sex, age, antiplatelet use, and gastrointestinal cancer (adjusted HR 2.80; 95% CI, 1.91-4.11). GDF-15 was also significantly associated with clinically relevant nonmajor bleeding (adjusted HR 1.67; 95% CI, 1.08-2.58) and any bleeding (adjusted HR 2.12; 95% CI, 1.38-3.25). CONCLUSIONS: Although hypothesis generating, this is the first study to show that GDF-15 predicts bleeding in cancer patients receiving thromboprophylaxis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".