{"id":"W4285341019","doi":"10.2196/34533","title":"Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Heparin-Induced Thrombocytopenia and Thrombosis","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Partial thromboplastin time; Medicine; Nomogram; Heparin; Prothrombin time; Therapeutic index; Activated clotting time; Internal medicine; Coagulation; Drug; Pharmacology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003125527,0.0007084562,0.0006440639,0.001850118,0.0002653183,0.0008708395,0.0008656316,0.0007655939,0.0008168381],"category_scores_gemma":[0.007333296,0.0002314449,0.0008057753,0.0008223562,0.000239526,0.0004902897,0.0004897931,0.001038792,0.000247535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009468772,"about_ca_system_score_gemma":0.0009223581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006672505,"about_ca_topic_score_gemma":0.004409123,"domain_scores_codex":[0.999072,0.000396678,0.00009648325,0.000221803,0.0001351551,0.00007788009],"domain_scores_gemma":[0.9953485,0.003514643,0.0004489753,0.0001141911,0.0004518976,0.0001217339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003291257,0.0007341101,0.2805403,0.000128701,0.0005045441,0.0002677243,0.0001500826,0.5813482,0.001054811,0.0006963541,0.002018944,0.132227],"study_design_scores_gemma":[0.00000901685,0.0001129157,0.008875952,0.00001833484,0.00003365665,0.00005237945,0.00002684151,0.9896772,0.0002932962,0.0006399729,0.0002524348,0.000008022736],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7466248,0.001770917,0.2449508,0.002048344,0.0001209692,0.0002445018,0.001466455,0.0005658627,0.002207218],"genre_scores_gemma":[0.9651313,0.0003142921,0.03288636,0.0001656601,0.00006429294,0.00009808132,0.0008165236,0.00001166083,0.0005117863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006672505,"threshold_uncertainty_score":0.01652962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1234726840238261,"score_gpt":0.4494514693875451,"score_spread":0.3259787853637189,"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."}}