{"id":"W4415735130","doi":"10.1182/bloodadvances.2025017862","title":"Derivation and external validation of a venous thromboembolism risk prediction model in asparaginase-treated ALL","year":2025,"lang":"en","type":"article","venue":"Blood Advances","topic":"Acute Lymphoblastic Leukemia research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Daiichi Sankyo Europe; Gilead Sciences; Servier; LEO Pharma; National Cancer Institute; Regeneron Pharmaceuticals; National Institutes of Health; Novo Nordisk; Swedish Orphan Biovitrum; Astellas Pharma; Seagen; Pfizer; Incyte; MorphoSys; Celgene; Alexion Pharmaceuticals; Jazz Pharmaceuticals; Teva Pharmaceutical Industries; Syndax Pharmaceuticals; BeiGene; Sanofi; Amgen; National Heart, Lung, and Blood Institute; Eli Lilly and Company","keywords":"Derivation; Confidence interval; Venous thromboembolism; Proportional hazards model; Cohort; Retrospective cohort study; Cohort study; Risk assessment; Pulmonary embolism","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01505464,0.0008241546,0.0006967955,0.0007535506,0.0003894936,0.001206139,0.001187493,0.0005653598,0.0008026483],"category_scores_gemma":[0.02034754,0.0003675673,0.0008177797,0.0004313742,0.0003608253,0.0003849357,0.00103664,0.001156761,0.0003211361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008870989,"about_ca_system_score_gemma":0.001887583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004328188,"about_ca_topic_score_gemma":0.002312485,"domain_scores_codex":[0.9975847,0.001553211,0.0001511811,0.000300007,0.0003152979,0.00009567143],"domain_scores_gemma":[0.9878832,0.008021979,0.001009207,0.0008901356,0.001913143,0.000282319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001806144,0.001154995,0.877678,0.00007854064,0.0007914823,0.0002596655,0.0001830657,0.08105326,0.001935567,0.0003183509,0.001236023,0.03350478],"study_design_scores_gemma":[0.0003191124,0.0009467073,0.139296,0.00006192384,0.0003374445,0.0003281706,0.00009379149,0.8540515,0.003367467,0.0004501407,0.0007199735,0.0000277915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842156,0.00007047944,0.01457028,0.000100103,0.00001782985,0.0001771707,0.0004470951,0.00009736083,0.0003041126],"genre_scores_gemma":[0.9909841,0.00003358738,0.007290727,0.00003399731,0.000008599875,0.0000873809,0.001427456,0.00001146016,0.0001225694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01505464,"threshold_uncertainty_score":0.0796175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105345349857376,"score_gpt":0.2943692381374073,"score_spread":0.2833157846388335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}