Global coagulation assays detect an early prothrombotic state in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: Pediatric patients with acute lymphoblastic leukemia (ALL) are at highest risk of venous thromboembolism during the induction therapy (IT). These events are not predictable by conventional coagulation assays. OBJECTIVES: To investigate the utility of global coagulation assays (GCAs) for assessing the hemostatic state in children with ALL during IT. METHODS: We included children with ALL (n = 15) and healthy controls (n = 15). Analyses were performed at different time points during IT of the AIEOP-BFM protocols. In addition to prothrombotic biomarkers, natural anticoagulant proteins, and in vivo thrombin generation (TG) markers, ex vivo TG was measured using the gold standard calibrated automated thrombogram method, automated ST Genesia, and thrombodynamics analyzer (TD). The latter also provided measurement of fibrin clot formation. RESULTS: Different from conventional coagulation assays and in vivo TG markers, ex vivo GCAs detected increasing prothrombotic changes during IT. Particularly, TG measured with TD as expressed by endogenous thrombin potential was already significantly elevated at days 8 to 12 (P < .01) and continued to increase during IT compared with prior to beginning treatment, indicating a very early shift toward a procoagulant state. A similar pattern was observed for the rate of fibrin clot formation (stationary rate of clot growth: P < .01 at days 8-12). Remarkably, in patients developing thrombotic complications (n = 5), both GCAs, ST Genesia and TD, showed a significantly higher endogenous thrombin potential very early (already at days 8-12, P < .05), well before clinical manifestation. CONCLUSION: GCAs capture prothrombotic changes early during IT in ALL pediatric patients. If confirmed, this approach will allow tailoring thromboprophylaxis in children with ALL at highest risk for venous thromboembolism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".