{"id":"W4310693661","doi":"10.1016/j.neuroimage.2022.119787","title":"Single-timepoint low-dimensional characterization and classification of acute versus chronic multiple sclerosis lesions using machine learning","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; NeuroRx Research (Canada); Montreal Neurological Institute and Hospital","funders":"Biogen","keywords":"Lesion; Medicine; Magnetic resonance imaging; Multiple sclerosis; Artificial intelligence; Radiology; Pattern recognition (psychology); Pathology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006421736,0.0006186409,0.000685787,0.0008817961,0.0001972591,0.0006812029,0.0006113644,0.0009687921,0.0004574262],"category_scores_gemma":[0.001732014,0.0001669981,0.0005914577,0.0005349745,0.000349572,0.0006630146,0.0005266537,0.000639313,0.0002933821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284817,"about_ca_system_score_gemma":0.0003165564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110386,"about_ca_topic_score_gemma":0.001366717,"domain_scores_codex":[0.9996876,0.00006161744,0.00001947167,0.0001236384,0.00006530201,0.00004243225],"domain_scores_gemma":[0.9994084,0.0002568447,0.000123025,0.00007586178,0.00009932643,0.00003655099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005307074,0.0003661338,0.02318512,0.0001489824,0.0001518856,0.0002332278,0.0001384189,0.2706862,0.07890152,0.001800774,0.001530791,0.6223263],"study_design_scores_gemma":[0.000006074299,0.0001135245,0.006324754,0.000008565882,0.00001944039,0.00008891182,0.0000211967,0.9827632,0.008863761,0.00145982,0.0003147359,0.00001599214],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2805008,0.0007141214,0.7169935,0.0001870199,0.00003634307,0.00005854519,0.0002143026,0.0006590676,0.0006363417],"genre_scores_gemma":[0.8614362,0.0002547538,0.1367429,0.00005224087,0.00005385534,0.00006010398,0.0003708926,0.00003919561,0.000989903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001110386,"threshold_uncertainty_score":0.003396213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05474468099063359,"score_gpt":0.2821085431625266,"score_spread":0.227363862171893,"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."}}