{"id":"W4284890890","doi":"10.1101/2022.07.06.22277151","title":"Advanced MRI scan acquisition metrics improve baseline disease severity predictions compared to traditional community MRI scan metrics","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Cervical and Thoracic Myelopathy","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute; Hotchkiss Brain Institute; Université de Montréal; University of Calgary","funders":"","keywords":"Magnetic resonance imaging; Medicine; Mri scan; Random forest; Radiology; Machine learning; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003269619,0.001195221,0.0009597422,0.001564946,0.0002540748,0.001325864,0.0005000982,0.0008747248,0.002218509],"category_scores_gemma":[0.01213783,0.0001833792,0.0007021647,0.0007148189,0.0002953026,0.001340305,0.0007224676,0.001154139,0.0008498694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004479647,"about_ca_system_score_gemma":0.0005817547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00602718,"about_ca_topic_score_gemma":0.005725329,"domain_scores_codex":[0.9987626,0.0004334082,0.0001282624,0.0003718958,0.0001895343,0.0001143225],"domain_scores_gemma":[0.9931055,0.002904253,0.001186617,0.0007191051,0.001423265,0.0006613473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004062398,0.001145905,0.7112984,0.0004106493,0.0009873558,0.0003065425,0.0001580704,0.06745888,0.01071984,0.000452915,0.007469859,0.1955292],"study_design_scores_gemma":[0.0001295598,0.00352755,0.3926877,0.0003219543,0.0006139056,0.0008351083,0.000267665,0.5822515,0.01154155,0.003152784,0.004521179,0.00014954],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592522,0.002725348,0.02906593,0.0007834401,0.0002528107,0.0001608257,0.004146882,0.001285137,0.002327457],"genre_scores_gemma":[0.9874696,0.0002474952,0.008266522,0.0001057402,0.0000869002,0.00003018478,0.003250196,0.00004287815,0.0005006543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00602718,"threshold_uncertainty_score":0.01729155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04627170737418054,"score_gpt":0.3074124936438669,"score_spread":0.2611407862696864,"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."}}