{"id":"W2902294772","doi":"10.1186/s13023-018-0955-7","title":"A nomenclature and classification for the congenital myasthenic syndromes: preparing for FAIR data in the genomic era","year":2018,"lang":"en","type":"article","venue":"Orphanet Journal of Rare Diseases","topic":"Myasthenia Gravis and Thymoma","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"FP7 Health; Horizon 2020; National Institute of Neurological Disorders and Stroke; Medical Research Council; Universiteit Leiden; National Institutes of Health; European Commission; Leids Universitair Medisch Centrum; Universitair Medisch Centrum Groningen","keywords":"Computer science; Expert system; Coding (social sciences); Disease; Medicine; Bioinformatics; Data science; Artificial intelligence; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004334954,0.0001151796,0.0002216186,0.00007328921,0.000135751,0.00007227792,0.0003873225,0.00004346335,0.00002996525],"category_scores_gemma":[0.0002576929,0.00005885538,0.00008983747,0.00008428454,0.0001158969,0.0001685313,0.00005076348,0.0001213439,0.000001913158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002599498,"about_ca_system_score_gemma":0.0001564258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003773939,"about_ca_topic_score_gemma":0.00002326622,"domain_scores_codex":[0.9991343,0.00004093181,0.000305526,0.0001705791,0.0001779668,0.0001706854],"domain_scores_gemma":[0.9986942,0.000426407,0.0002210903,0.0004267806,0.0001442405,0.00008728277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03988069,0.003877909,0.3488398,0.003074174,0.005710302,0.0006234237,0.02517623,0.00002434338,0.02594488,0.01089662,0.1972319,0.3387198],"study_design_scores_gemma":[0.008501898,0.003193128,0.8384238,0.0004803525,0.002225224,0.00418982,0.004739894,0.006050464,0.00006660847,0.003265034,0.1285362,0.0003276018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875007,0.007862307,0.001078063,0.001934917,0.0001615218,0.0009975435,0.0003977837,0.000006805297,0.00006032634],"genre_scores_gemma":[0.9981517,0.0001729485,0.0005581764,0.0003118611,0.0005950844,0.00003404887,0.00009746369,0.00001678223,0.00006199546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.489584,"threshold_uncertainty_score":0.2400053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253911472322085,"score_gpt":0.3123622424009337,"score_spread":0.2698231276777128,"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."}}