{"id":"W4297222196","doi":"10.1038/s41467-022-33269-x","title":"Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroRx Research (Canada); McGill University; Mila - Quebec Artificial Intelligence Institute; McGill Genome Centre; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Multiple Sclerosis Society; Genentech; Government of Canada; MedDay Pharmaceuticals; Multiple Sclerosis Society of Canada; Ministère de la Santé; Teva Pharmaceutical Industries; Ministère de la Santé et des Services sociaux; International Progressive MS Alliance; Canadian Institute for Advanced Research; Biogen","keywords":"Medicine; Multiple sclerosis; Placebo; Randomized controlled trial; Clinical trial; Biomarker; Internal medicine; Oncology; Pathology; Immunology","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":[0.01253062,0.0009108165,0.001803602,0.0005332932,0.0001751108,0.0005735737,0.0007045504,0.0009874494,0.001407866],"category_scores_gemma":[0.01930002,0.0003562603,0.002043053,0.0003559313,0.0006314543,0.0007773123,0.0007931415,0.002536075,0.0002089929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007084554,"about_ca_system_score_gemma":0.000880785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002731905,"about_ca_topic_score_gemma":0.003799742,"domain_scores_codex":[0.9963539,0.002749842,0.0001409905,0.000409652,0.0001771071,0.0001686125],"domain_scores_gemma":[0.9835126,0.01446625,0.0009207859,0.0005727498,0.000260733,0.0002667861],"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":[0.02154975,0.00143037,0.1007422,0.001377248,0.007450346,0.0001983396,0.0001408776,0.54936,0.005586671,0.002369234,0.00351258,0.3062824],"study_design_scores_gemma":[0.002355659,0.004860623,0.04744818,0.000200897,0.00356445,0.000137801,0.00003867366,0.9193387,0.005275379,0.01483607,0.001871249,0.0000723632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8825397,0.009774876,0.09892925,0.003874544,0.0002156574,0.000218409,0.001637326,0.00071078,0.002099372],"genre_scores_gemma":[0.9928434,0.0004532951,0.00503454,0.0004885911,0.00006288156,0.00008378011,0.0004969458,0.00001491102,0.0005216419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01253062,"threshold_uncertainty_score":0.06626898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1439934442281792,"score_gpt":0.4160894975207688,"score_spread":0.2720960532925896,"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."}}