{"id":"W4302362400","doi":"10.32920/21287934.v1","title":"Measurement Variability Following MRI System Upgrade","year":2022,"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; Nuclear medicine; Magnetic resonance imaging; 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":[0.007018958,0.0003912256,0.0004603325,0.0008057204,0.000319224,0.0007161423,0.0005288044,0.0004705498,0.000915469],"category_scores_gemma":[0.02501375,0.0002171784,0.0003595631,0.0008327125,0.0003606947,0.0004416194,0.001021696,0.000549087,0.0005358597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003486697,"about_ca_system_score_gemma":0.0001837506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009123664,"about_ca_topic_score_gemma":0.001229499,"domain_scores_codex":[0.9937116,0.002045284,0.0007856374,0.001673088,0.001529643,0.0002548209],"domain_scores_gemma":[0.9776243,0.009502013,0.003539432,0.005041995,0.003946053,0.0003462117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004269246,0.0002993399,0.5622388,0.0004523925,0.001024795,0.00157168,0.002483965,0.004307196,0.1190479,0.0003695867,0.005057019,0.2988781],"study_design_scores_gemma":[0.00003357516,0.001391819,0.9500747,0.00003260572,0.0002379756,0.002578082,0.0002975618,0.004884468,0.03438557,0.000459332,0.00555458,0.0000696843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.955615,0.001431012,0.03757511,0.0002509186,0.0002306048,0.0001360143,0.001233659,0.0009710391,0.002556768],"genre_scores_gemma":[0.9907355,0.000168268,0.006528199,0.000212666,0.0000724753,0.0001018308,0.001200087,0.0002238447,0.0007570903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007018958,"threshold_uncertainty_score":0.03712022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312085841303735,"score_gpt":0.2983654682356776,"score_spread":0.2752446098226403,"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."}}