{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008751079,0.0001793551,0.000496876,0.00004793077,0.0002409398,0.00005908773,0.001321836,0.00003796337,0.0001020407],"category_scores_gemma":[0.00007997631,0.0001377963,0.00001949785,0.000247257,0.00002463256,0.0002371728,0.0009989246,0.0002662182,0.00006781325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001005304,"about_ca_system_score_gemma":0.001094687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002466494,"about_ca_topic_score_gemma":0.0006378235,"domain_scores_codex":[0.997835,0.0002402149,0.0005869204,0.0006635834,0.0002088221,0.0004655043],"domain_scores_gemma":[0.9975336,0.0001199541,0.000276476,0.001619836,0.00003083234,0.0004193067],"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":[0.01273248,0.001475426,0.238693,0.04220904,0.000422764,0.00002530837,0.008306452,0.00004159933,0.0001409202,0.00138325,0.1084864,0.5860834],"study_design_scores_gemma":[0.0150966,0.00838314,0.4320987,0.08191483,0.0001793293,0.0001103813,0.000394916,0.04273459,0.0002675557,0.0007466937,0.416878,0.001195309],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.477782,0.06889935,0.001249507,0.4235826,0.001049894,0.0216282,0.00361475,0.0002420907,0.001951638],"genre_scores_gemma":[0.9731228,0.0009241493,0.003903362,0.01839539,0.0002654213,0.0000635251,0.002566238,0.00005469067,0.0007044433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.584888,"threshold_uncertainty_score":0.5619172,"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."}}