{"id":"W3099918881","doi":"10.1002/pds.5176","title":"Using artificial intelligence to identify anti‐hypertensives as possible disease modifying agents in Parkinson's disease","year":2020,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital","funders":"Ontario Brain Institute","keywords":"Medicine; Disease; Parkinson's disease; Pharmacoepidemiology; Intensive care medicine; Internal medicine; Pharmacology; Medical prescription","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005829217,0.0003568603,0.0007076453,0.0001777241,0.0002374211,0.0000214727,0.0001416051,0.00007294351,0.0002023883],"category_scores_gemma":[0.00133019,0.0003217901,0.0001698396,0.0003567607,0.0001182378,0.000184404,0.0001904551,0.0002898651,0.0001354285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008309565,"about_ca_system_score_gemma":0.000190962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001407271,"about_ca_topic_score_gemma":0.000003884334,"domain_scores_codex":[0.9971623,0.0005169481,0.0006163293,0.0009054132,0.0002024625,0.0005964817],"domain_scores_gemma":[0.9972488,0.0003265637,0.0001302662,0.0002357715,0.00006959899,0.001988944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01336111,0.001070969,0.9314579,0.0005270402,0.0004050501,0.006000806,0.0008907989,0.006645183,0.004535749,0.00454946,0.0007624801,0.02979339],"study_design_scores_gemma":[0.001561194,0.0001128201,0.8360506,0.0003341071,0.0009551363,0.00003463028,0.0003681822,0.1380689,0.001950121,0.01186056,0.008054776,0.000648982],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695695,0.003114606,0.008282447,0.017613,0.0002958681,0.0008160069,0.0001243472,0.00008924698,0.00009499022],"genre_scores_gemma":[0.9769618,0.001203473,0.001512605,0.01991927,0.0002408023,0.00004341897,0.0000664033,0.00003387777,0.00001837469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1314237,"threshold_uncertainty_score":0.9999234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1709062061979557,"score_gpt":0.4247803904908772,"score_spread":0.2538741842929216,"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."}}