{"id":"W2593373425","doi":"10.1177/1352458517698250","title":"Landscape of MS patient cohorts and registries: Recommendations for maximizing impact","year":2017,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Multiple Sclerosis Society of Canada","funders":"National Institutes of Health; IC Design Education Center; Eisai; International Progressive MS Alliance; Cleveland Clinic; Multiple Sclerosis Society; Biogen; National Multiple Sclerosis Society","keywords":"Multiple sclerosis; Medicine; MEDLINE; Clinical neurology; Psychology; Psychiatry; Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007061461,0.0002087395,0.0005711255,0.0001892358,0.001314927,0.0002407179,0.0002060622,0.00008536389,0.0001276471],"category_scores_gemma":[0.004080616,0.0001571148,0.0002719408,0.00006544696,0.0002749802,0.0003138386,0.0001900255,0.0003184418,0.000002093748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001050683,"about_ca_system_score_gemma":0.0001220362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001609978,"about_ca_topic_score_gemma":0.00005362924,"domain_scores_codex":[0.9982545,0.00005791057,0.0005688837,0.0002640102,0.0004105185,0.0004442075],"domain_scores_gemma":[0.9974505,0.0004083527,0.0006582667,0.0005378325,0.0005860819,0.000358999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001008753,0.0003083657,0.7680497,0.0001560318,0.0006066267,0.000006553045,0.0012362,0.00002224142,0.0300423,0.00001109115,0.03862217,0.15993],"study_design_scores_gemma":[0.006127167,0.0008727561,0.9763925,0.0009159501,0.0001113941,0.00008551119,0.0007271488,0.001978661,0.004902546,0.00003883418,0.007659506,0.0001880133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902112,0.0007794712,0.0007069583,0.006315765,0.0003042919,0.0008766713,0.0002445617,0.0000244668,0.0005366033],"genre_scores_gemma":[0.9866014,0.00366269,0.009273738,0.00005799683,0.0002350089,0.00005296033,0.00002284868,0.00003093562,0.00006238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2083428,"threshold_uncertainty_score":0.9999852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1587756045274569,"score_gpt":0.3640429735893991,"score_spread":0.2052673690619422,"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."}}