{"id":"W4289712074","doi":"10.1101/2022.08.01.22271457","title":"Quantitative MRI and histopathology detect remyelination in inactive multiple sclerosis lesions","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Research Chairs; Multiple Sclerosis Society of Canada; European Genomic Institute for Diabetes; Biogen; BC Children's Hospital; Multiple Sclerosis Society","keywords":"Myelin; Remyelination; Multiple sclerosis; White matter; Pathology; Histopathology; Magnetic resonance imaging; Histology; Magnetization transfer; Hyperintensity; Medicine; Biology; Central nervous system; Immunology; Radiology; Internal 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":[0.002316579,0.0005385305,0.0004292154,0.0009527399,0.00009427148,0.0003876346,0.00033341,0.0004915271,0.0005651604],"category_scores_gemma":[0.004448168,0.0003507981,0.0004797342,0.0002098579,0.0006387085,0.0003734099,0.0001949332,0.0002680958,0.0001727009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002570447,"about_ca_system_score_gemma":0.0001349754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001385715,"about_ca_topic_score_gemma":0.001495487,"domain_scores_codex":[0.9994616,0.0002242915,0.00004671929,0.000147099,0.0000816367,0.00003872386],"domain_scores_gemma":[0.997506,0.001237673,0.0007571293,0.0002416153,0.0001754167,0.00008232237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003590157,0.0003210191,0.396605,0.0006088073,0.0009378545,0.0008200453,0.0008155362,0.02853387,0.5253664,0.000671229,0.000145767,0.0415844],"study_design_scores_gemma":[0.00004654497,0.001639581,0.9113116,0.00003099316,0.0003702449,0.001371129,0.0001448758,0.04067446,0.04291688,0.001019054,0.0004243004,0.00005023658],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931416,0.0003431631,0.006142362,0.00001188915,0.000001944144,0.00002121436,0.00008395285,0.00005728085,0.0001966801],"genre_scores_gemma":[0.9978591,0.00004738177,0.001822664,0.000006195359,0.00000243872,0.00001045357,0.0001014641,0.00001311786,0.0001371008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002316579,"threshold_uncertainty_score":0.01225138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1487070206028842,"score_gpt":0.3651460283018403,"score_spread":0.2164390076989561,"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."}}