{"id":"W4405996645","doi":"10.1161/atvbaha.124.321673","title":"Artificial Intelligence and Machine Learning in Preeclampsia","year":2025,"lang":"en","type":"review","venue":"Arteriosclerosis Thrombosis and Vascular Biology","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Preeclampsia; Artificial intelligence; Computer science; Medicine; Disease; Pregnancy; Machine learning; Data science; Bioinformatics; Pathology; Biology","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":[0.0008578843,0.0006840194,0.001204723,0.002663768,0.0003120191,0.001157006,0.000604612,0.001345267,0.002831203],"category_scores_gemma":[0.00194117,0.0002304241,0.0006316782,0.003298807,0.0006427985,0.001537367,0.0007178396,0.002190779,0.001198051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008436273,"about_ca_system_score_gemma":0.001702936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001506739,"about_ca_topic_score_gemma":0.001912293,"domain_scores_codex":[0.9996411,0.0001111455,0.00006383099,0.00004614415,0.0001147581,0.00002296691],"domain_scores_gemma":[0.9990415,0.0007029653,0.00006518461,0.00002070426,0.0001347559,0.00003495185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004895121,0.00006889278,0.0002686236,0.02577109,0.0001593295,0.0001804488,0.0001390395,0.0005894691,0.0003766668,0.0112149,0.03465547,0.9265271],"study_design_scores_gemma":[0.00001466464,0.0000899073,0.001525191,0.01467868,0.000168517,0.001068973,0.0001168787,0.0002565313,0.0002513069,0.01322502,0.9685699,0.0000344585],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005203643,0.9984306,0.0001291636,0.0005080673,0.0001908521,0.000003207846,0.000008999938,0.000004421903,0.0006726421],"genre_scores_gemma":[0.0004334522,0.9987726,0.0001528259,0.0002033547,0.0002120192,0.000004613201,0.00001178339,9.434991e-7,0.0002083825],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002831203,"threshold_uncertainty_score":0.009471297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.124650778211735,"score_gpt":0.3533352905217885,"score_spread":0.2286845123100535,"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."}}