{"id":"W4387934035","doi":"10.32920/24438073","title":"Measurement Variability Following MRI System Upgrade","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Montréal; Institut Universitaire de Gériatrie de Montréal; Toronto Metropolitan University; Montreal Neurological Institute and Hospital; Douglas Mental Health University Institute; McGill University","funders":"Pfizer Canada; Fonds de Recherche du Québec - Santé; Alzheimer's Society; Consortium canadien en neurodégénérescence associée au vieillissement; Pfizer","keywords":"Fluid-attenuated inversion recovery; Upgrade; Hyperintensity; Context (archaeology); Siemens; Magnetic resonance imaging; Nuclear medicine; Medicine; White matter; Radiology; Computer science; Physics; Biology","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":[0.006402216,0.0004367851,0.0004951886,0.0008746986,0.0003166216,0.0007910094,0.0005050376,0.0005017608,0.000930316],"category_scores_gemma":[0.02577749,0.0002437368,0.0003534021,0.0009017555,0.0003620569,0.0004569784,0.0009970024,0.000560449,0.0005809781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031948,"about_ca_system_score_gemma":0.0001707594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007784494,"about_ca_topic_score_gemma":0.001024055,"domain_scores_codex":[0.9940533,0.00187515,0.0006907404,0.001601476,0.001526771,0.000252523],"domain_scores_gemma":[0.9784639,0.009320122,0.003202935,0.004865302,0.003807209,0.0003404488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005192145,0.0003037312,0.5392414,0.0006255041,0.001241946,0.001677043,0.003139819,0.005262979,0.1449626,0.0004628691,0.005613826,0.2922761],"study_design_scores_gemma":[0.00004067112,0.001381023,0.9397129,0.00004063413,0.0003150972,0.002951144,0.0003567896,0.005646437,0.04238148,0.0005989072,0.006495403,0.00007939465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582157,0.001380502,0.03505964,0.0002266039,0.0002363469,0.0001312395,0.001309751,0.000979071,0.002461246],"genre_scores_gemma":[0.9904654,0.0001826342,0.006612794,0.0001855768,0.00008355035,0.0001003897,0.001378758,0.0002552304,0.0007356751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006402216,"threshold_uncertainty_score":0.03385854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972259078700409,"score_gpt":0.3118656727692562,"score_spread":0.2721430819822521,"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."}}