{"id":"W4283371744","doi":"10.1017/cjn.2022.86","title":"GP.2 Deep learning prediction of response to disease modifying therapy in primary progressive multiple sclerosis","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Ocrelizumab; Multiple sclerosis; Placebo; Randomized controlled trial; Clinical trial; Internal medicine; Rituximab; Oncology; Physical therapy; Pathology; 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":[0.002287408,0.0008839436,0.0008578369,0.0004957717,0.0002193304,0.0006374235,0.0009043985,0.001348583,0.002283825],"category_scores_gemma":[0.004108206,0.0003346497,0.000793641,0.0004304588,0.0003591613,0.0003572125,0.0007600384,0.001475997,0.0005602503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007940858,"about_ca_system_score_gemma":0.00120197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009037128,"about_ca_topic_score_gemma":0.004920038,"domain_scores_codex":[0.9996482,0.0001474904,0.00001635963,0.00008296601,0.00004315684,0.00006191927],"domain_scores_gemma":[0.9989893,0.0006717774,0.00007262776,0.0000657652,0.0001416161,0.00005889608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001312186,0.0003966112,0.02024836,0.0002156481,0.0004458836,0.0001585267,0.00005770837,0.8291056,0.001968508,0.001224741,0.009483431,0.1353828],"study_design_scores_gemma":[0.00008271632,0.0001734163,0.001890107,0.00001783675,0.00004466524,0.00002883769,0.000004644212,0.9952652,0.0004995447,0.001530901,0.000456004,0.000006107055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7028221,0.006395323,0.2661692,0.004811625,0.0004948681,0.0003301566,0.005963965,0.005481539,0.007531175],"genre_scores_gemma":[0.9641965,0.0006120041,0.02726438,0.000661565,0.0001166333,0.0002709401,0.002555831,0.00007669933,0.004245318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009037128,"threshold_uncertainty_score":0.01796907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04024238356655961,"score_gpt":0.2768000189449339,"score_spread":0.2365576353783743,"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."}}