{"id":"W4413440516","doi":"10.21203/rs.3.rs-7361397/v1","title":"Longitudinal Visualization Tools for Advanced Characterization of Multiple Sclerosis Lesions Using Diffusion MRI and Magnetization Transfer Imaging Metrics","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs; Université de Sherbrooke","keywords":"Diffusion MRI; Lesion; Magnetization transfer; Multiple sclerosis; White matter; Computer science; Visualization; Radiology; Magnetic resonance imaging; Artificial intelligence; Pathology; Medicine","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":[0.001830373,0.001123435,0.0005992017,0.003383925,0.000385812,0.003106113,0.0007504699,0.0009954002,0.007187457],"category_scores_gemma":[0.01010896,0.0006076582,0.000593546,0.001741288,0.0003298639,0.002895343,0.001617539,0.001100036,0.001930201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003232411,"about_ca_system_score_gemma":0.001017176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309386,"about_ca_topic_score_gemma":0.001500497,"domain_scores_codex":[0.9996093,0.0001195616,0.00005058136,0.00006496496,0.0001234385,0.00003217613],"domain_scores_gemma":[0.996199,0.001481306,0.0007205746,0.0004764361,0.0009080321,0.0002146815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008066765,0.0002934936,0.02087947,0.0006398506,0.0002119007,0.0007844354,0.000998945,0.03755427,0.1216885,0.04418175,0.02425009,0.7477107],"study_design_scores_gemma":[0.00009141781,0.0002430046,0.01253917,0.0001825602,0.0001289643,0.001749843,0.0004582274,0.8127993,0.07993837,0.06053741,0.03118426,0.0001474188],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02825398,0.0007209451,0.9613914,0.0006022202,0.00005933611,0.00006621188,0.001169463,0.006533026,0.001203389],"genre_scores_gemma":[0.2149743,0.0009851311,0.7775559,0.00007940123,0.0001409529,0.000261014,0.001571145,0.001811395,0.002620714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007187457,"threshold_uncertainty_score":0.02404445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.278261803017091,"score_gpt":0.4659820557567396,"score_spread":0.1877202527396487,"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."}}