{"id":"W4292466559","doi":"10.1177/13524585221112605","title":"Machine learning classification of multiple sclerosis in children using optical coherence tomography","year":2022,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Toronto Metropolitan University; SickKids Foundation; University of Toronto; Vector Institute; University of Manitoba; Hospital for Sick Children","funders":"","keywords":"Multiple sclerosis; Optical coherence tomography; Random forest; Retinal; Clinically isolated syndrome; Demyelinating Disorder; Medicine; Artificial intelligence; Pattern recognition (psychology); Computer science; Ophthalmology; Pathology","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":[0.0008248169,0.000430089,0.0002911351,0.001477899,0.0001667968,0.0004023047,0.0002243004,0.0003251149,0.0007178827],"category_scores_gemma":[0.003306213,0.00009031621,0.0003680254,0.0004891791,0.000179477,0.0003053352,0.0002298036,0.0004047378,0.0002266791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004315659,"about_ca_system_score_gemma":0.000356981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004534403,"about_ca_topic_score_gemma":0.003853765,"domain_scores_codex":[0.9996703,0.0001164583,0.00003560319,0.0000578654,0.00007136448,0.00004835813],"domain_scores_gemma":[0.9983501,0.0008840599,0.0004004647,0.00006187704,0.0002175528,0.00008595586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002433649,0.00007709439,0.9497381,0.00003947827,0.00007240959,0.0002527316,0.0001008741,0.005341522,0.002138615,0.0001088789,0.0007046524,0.04118227],"study_design_scores_gemma":[0.00002614723,0.0003714494,0.8697138,0.00007600303,0.00008497911,0.00128287,0.0004941342,0.1209645,0.005266344,0.0005893963,0.001107452,0.00002285823],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951075,0.0003020997,0.003485211,0.000096416,0.00001117033,0.00002610749,0.0005372527,0.00004785276,0.0003863856],"genre_scores_gemma":[0.9947507,0.000105017,0.004558747,0.00001273797,0.000009511798,0.00001955155,0.0004201914,0.000004433185,0.0001191718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004534403,"threshold_uncertainty_score":0.009015977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1360166471492897,"score_gpt":0.3078742062840037,"score_spread":0.171857559134714,"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."}}