{"id":"W4414370752","doi":"10.1101/2025.09.17.25335756","title":"Tract-based Quantitative MRI for Resolving the Clinico-Radiological Paradox in Multiple Sclerosis","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tamkeen; Multiple Sclerosis Society; York University; New York University Abu Dhabi","keywords":"Multiple sclerosis; Diffusion MRI; Fractional anisotropy; Lesion; White matter; Magnetic resonance imaging; Expanded Disability Status Scale; Neuroradiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001768289,0.000375435,0.0003161893,0.001109369,0.0001853395,0.0004426039,0.0003140692,0.0004346278,0.001254945],"category_scores_gemma":[0.005861578,0.000170339,0.0002444276,0.0005841338,0.0003229564,0.000491979,0.000306801,0.0002584523,0.000189769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002434444,"about_ca_system_score_gemma":0.0002419448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00240022,"about_ca_topic_score_gemma":0.003977537,"domain_scores_codex":[0.9996673,0.0002110809,0.00002221676,0.0000510543,0.00003276359,0.00001559598],"domain_scores_gemma":[0.9978148,0.0009673046,0.0006328988,0.0002760849,0.0002120063,0.00009694643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003650014,0.000244284,0.750832,0.0003346239,0.0007170489,0.000686203,0.0004811764,0.03304791,0.09964847,0.001868638,0.0008997651,0.1075898],"study_design_scores_gemma":[0.00009430461,0.0007638467,0.6410736,0.00005714531,0.0001819236,0.001629112,0.0003035144,0.3409435,0.009618382,0.004173159,0.001091796,0.00006973595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714613,0.0003321288,0.02720705,0.00008736636,0.000005278769,0.00004341106,0.0003883318,0.0001473217,0.0003278325],"genre_scores_gemma":[0.9906774,0.0000498718,0.008985399,0.000006427732,0.000005848779,0.00001549628,0.0001499729,0.00001572928,0.00009391949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00240022,"threshold_uncertainty_score":0.00935173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1167171555869915,"score_gpt":0.3739828500924988,"score_spread":0.2572656945055073,"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."}}