{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001206558,0.0002091984,0.0006023737,0.0001544125,0.0001017195,0.00001765022,0.0002163644,0.0001514599,0.00002108259],"category_scores_gemma":[0.004681591,0.0001443761,0.00009330346,0.0004836467,0.0001874677,0.0001755128,0.0002966892,0.0004931782,0.000004206065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419839,"about_ca_system_score_gemma":0.000646804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004978725,"about_ca_topic_score_gemma":0.000004629279,"domain_scores_codex":[0.9965611,0.00003438461,0.001221739,0.0001771759,0.001586608,0.0004190144],"domain_scores_gemma":[0.9984943,0.0003337382,0.0002648933,0.0001526578,0.0003405103,0.0004138892],"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":[0.0003829342,0.0006685364,0.002389023,0.002942491,0.0001272515,0.000001097169,0.0835012,0.00002623269,0.006120316,0.00001146224,0.0002837328,0.9035457],"study_design_scores_gemma":[0.007212102,0.0006273236,0.006485727,0.003204003,0.00001717298,0.000003343043,0.02056837,0.9529756,0.008452583,0.000001880387,0.0002829586,0.0001689561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3421574,0.0001599897,0.650944,0.0006507696,0.00005585233,0.005258974,0.00002917223,0.0001060817,0.0006377076],"genre_scores_gemma":[0.2936126,0.00003049568,0.7047598,0.0002683174,0.00007073028,0.001177334,0.00005988247,0.00001585611,0.00000500173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9529493,"threshold_uncertainty_score":0.5887486,"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."}}