{"id":"W3015945108","doi":"10.2196/17592","title":"A Novel, Integrative Approach for Evaluating Progression in Multiple Sclerosis: Development of a Scoring Algorithm","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novartis Pharma","keywords":"Weighting; Concordance; Ranking (information retrieval); Logistic regression; Observational study; Medicine; Multiple sclerosis; Machine learning; Algorithm; Physical therapy; Computer science; Internal medicine; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03756813,0.002328831,0.0017641,0.007265473,0.001474361,0.00383074,0.003369524,0.001348557,0.003603499],"category_scores_gemma":[0.08686136,0.0008269331,0.002568801,0.005187717,0.001671573,0.004520732,0.004350364,0.00256427,0.00126979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002895295,"about_ca_system_score_gemma":0.006818905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003684599,"about_ca_topic_score_gemma":0.005378564,"domain_scores_codex":[0.9733161,0.01532709,0.003500277,0.002008469,0.005352559,0.0004955432],"domain_scores_gemma":[0.9567774,0.02174581,0.00324476,0.001758451,0.015748,0.0007254675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003821343,0.0005095039,0.044426,0.001397051,0.000463105,0.0002539819,0.003789349,0.01667805,0.002992805,0.0265072,0.01241843,0.8901824],"study_design_scores_gemma":[0.0006486888,0.002322929,0.05059553,0.003169132,0.001170384,0.002737294,0.007357982,0.7446442,0.008225459,0.1085971,0.06996602,0.0005652684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02140322,0.000498742,0.9669358,0.001165832,0.0001475183,0.004581886,0.0004711551,0.001098585,0.003697219],"genre_scores_gemma":[0.02668506,0.000146604,0.970288,0.0000586017,0.0000186965,0.002177212,0.0002623865,0.00004358212,0.000319839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03756813,"threshold_uncertainty_score":0.1986817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2305295143142653,"score_gpt":0.4153440751055314,"score_spread":0.1848145607912661,"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."}}