{"id":"W4389263564","doi":"10.3389/fneur.2023.1319869","title":"Neuroimaging to monitor worsening of multiple sclerosis: advances supported by the grant for multiple sclerosis innovation","year":2023,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Qatar National Research Fund; EMD Serono; Genentech; National Institutes of Health; Novo Nordisk; Fonds National de la Recherche Luxembourg; Multiple Sclerosis Society of Canada; TG Therapeutics; Horizon Therapeutics; Hamad Medical Corporation; University of Texas System; Biogen; Celgene; Alexion Pharmaceuticals; Merck KGaA; Sanofi; Bristol-Myers Squibb; Eli Lilly and Company","keywords":"Neuroimaging; Multiple sclerosis; Clinical trial; Medicine; Magnetic resonance imaging; Positron emission tomography; Disease; Medical physics; Intensive care medicine; Pathology; Psychiatry; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01469259,0.0006343152,0.0007098791,0.002031205,0.0003657811,0.002147862,0.0007075304,0.001658465,0.004160345],"category_scores_gemma":[0.020024,0.0001781692,0.0004381418,0.001740971,0.001368648,0.002945187,0.002313961,0.002749061,0.001473067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002195346,"about_ca_system_score_gemma":0.006245658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003860388,"about_ca_topic_score_gemma":0.004295888,"domain_scores_codex":[0.9971092,0.001224807,0.0002991431,0.0002656319,0.0008920532,0.0002091419],"domain_scores_gemma":[0.9829364,0.007519477,0.001707867,0.0005838641,0.005978617,0.001273802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002743071,0.00009973552,0.004009745,0.002689105,0.0001077474,0.0001894094,0.000139777,0.0004050742,0.001709164,0.01989654,0.08325834,0.8872211],"study_design_scores_gemma":[0.0001897327,0.0006560423,0.0297136,0.008226817,0.0002367807,0.001645243,0.0002732036,0.0009770845,0.003531809,0.04846126,0.9059772,0.0001111055],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.004690005,0.7899522,0.0076508,0.162511,0.006431455,0.0001765164,0.0006154321,0.0001995085,0.02777309],"genre_scores_gemma":[0.0507364,0.9025413,0.01733596,0.01408606,0.009494575,0.0002675139,0.0006361296,0.0001064595,0.004795722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01469259,"threshold_uncertainty_score":0.07770276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07866688941041565,"score_gpt":0.3114635282845238,"score_spread":0.2327966388741081,"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."}}