{"id":"W2977524947","doi":"10.12688/f1000research.13114.1","title":"Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study","year":2017,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Consiglio Nazionale delle Ricerche; Fondazione Italiana Sclerosi Multipla","keywords":"Machine learning; Artificial intelligence; Disease; Multiple sclerosis; Set (abstract data type); Medicine; Crowdsourcing; Computer science; Internal medicine; Immunology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003255154,0.0004254711,0.001287394,0.0006286073,0.0005625082,0.0001873413,0.0007191224,0.0004034195,0.00004147138],"category_scores_gemma":[0.003350199,0.0003955611,0.0001588758,0.0004110648,0.00124787,0.0001807391,0.002813584,0.001499463,0.000002253851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003135826,"about_ca_system_score_gemma":0.0009432209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003805298,"about_ca_topic_score_gemma":0.003107083,"domain_scores_codex":[0.9941164,0.0005183254,0.001238637,0.001074184,0.002383973,0.0006684504],"domain_scores_gemma":[0.9946812,0.0005341449,0.0006784823,0.001689553,0.002038616,0.0003780133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00094957,0.003158147,0.7981787,0.003611849,0.001387739,0.00001625641,0.006050775,0.00005186824,0.1186471,0.0001385747,0.0002925034,0.06751694],"study_design_scores_gemma":[0.00140516,0.003872112,0.9079649,0.001398274,0.0003294889,8.203004e-7,0.003418579,0.004234394,0.07650583,0.0005114118,0.00007647485,0.0002825794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862754,0.0004684306,0.0004197284,0.0006246457,0.000201283,0.009396245,0.002321806,0.0000654635,0.0002270099],"genre_scores_gemma":[0.9966748,0.0003891325,0.0005447516,0.000002527868,0.0005528902,0.001129331,0.0004668374,0.00007540648,0.0001642708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1097862,"threshold_uncertainty_score":0.9998496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2542008372520985,"score_gpt":0.4396177990789156,"score_spread":0.1854169618268171,"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."}}