{"id":"W4283390496","doi":"10.1017/cjn.2022.201","title":"P.107 Personalized prediction of future lesion activity and treatment effect in multiple sclerosis from baseline MRI","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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Lesion; Multiple sclerosis; Placebo; Clinical trial; Limiting; Treatment effect; Randomized controlled trial; Baseline (sea); Internal medicine; Surgery; Pathology; Alternative 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":[0.002865972,0.0006063721,0.0007789648,0.0006188399,0.0001964228,0.0008139198,0.0006134,0.0008930537,0.003710818],"category_scores_gemma":[0.009810345,0.0002425772,0.0009246195,0.0003983021,0.0005220108,0.000755829,0.0003609119,0.001598348,0.0007276956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008862701,"about_ca_system_score_gemma":0.0008776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005181345,"about_ca_topic_score_gemma":0.006141103,"domain_scores_codex":[0.9993629,0.0002927508,0.00003900794,0.0001800744,0.00008807048,0.00003733747],"domain_scores_gemma":[0.9957948,0.003264087,0.0004595442,0.0001595521,0.0002328978,0.00008920263],"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":[0.001823428,0.0004466365,0.1088288,0.000665231,0.0007632808,0.0002735787,0.0001029556,0.48225,0.002005956,0.004929713,0.01848754,0.379423],"study_design_scores_gemma":[0.0002476938,0.0005593181,0.03345192,0.000220985,0.000292957,0.000295389,0.00002498772,0.9329081,0.001826006,0.02527747,0.004850576,0.00004463302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4807555,0.01216504,0.4505315,0.02129954,0.0005619164,0.0005603599,0.01457177,0.002419998,0.01713431],"genre_scores_gemma":[0.9651108,0.0008882557,0.02835942,0.0009705253,0.0001868934,0.0001638766,0.001716117,0.00004662099,0.002557533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005181345,"threshold_uncertainty_score":0.01515687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0395460189932205,"score_gpt":0.2711937267131004,"score_spread":0.2316477077198799,"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."}}