{"id":"W3081843254","doi":"10.1192/bjo.2020.78","title":"Schizophrenia around the time of pregnancy: leveraging population-based health data and electronic health record data to fill knowledge gaps","year":2020,"lang":"en","type":"article","venue":"BJPsych Open","topic":"Maternal Mental Health During Pregnancy and Postpartum","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women's College Hospital","funders":"Women's College Hospital","keywords":"Schizophrenia (object-oriented programming); Health data; Health records; Electronic health record; Population; Data science; Psychology; Psychiatry; Computer science; Medicine; Environmental health; Political science; Health care","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.03398997,0.0004442921,0.001002466,0.01065709,0.000709289,0.004130258,0.001118706,0.001421114,0.001105465],"category_scores_gemma":[0.1267128,0.0006645528,0.001273348,0.0121033,0.001426579,0.006818567,0.003843042,0.002288834,0.0001808302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002441096,"about_ca_system_score_gemma":0.008921362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03112652,"about_ca_topic_score_gemma":0.04418668,"domain_scores_codex":[0.9759045,0.01615741,0.00325603,0.001646657,0.0024807,0.0005547217],"domain_scores_gemma":[0.8442772,0.1278291,0.01465815,0.005157533,0.006988298,0.001089774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002681817,0.00009078479,0.2736008,0.0335266,0.003775577,0.001484546,0.02210896,0.001418028,0.0005264049,0.01777652,0.02934357,0.6160801],"study_design_scores_gemma":[0.0000744094,0.0003212842,0.4337482,0.1730735,0.004380778,0.002724536,0.03753272,0.002609869,0.001098672,0.04578092,0.298311,0.0003440252],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1133043,0.7216106,0.02275763,0.1042692,0.001823028,0.0005787638,0.02200355,0.0001664287,0.01348648],"genre_scores_gemma":[0.4330904,0.5096205,0.03571702,0.01017359,0.001648109,0.0007338524,0.008406678,0.00007536919,0.0005343515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03398997,"threshold_uncertainty_score":0.1797584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1199042324533791,"score_gpt":0.3911884483756002,"score_spread":0.2712842159222211,"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."}}