{"id":"W7045233164","doi":"","title":"Adult genetic leukoencephalopathies: identifying new entities using advanced MRI techniques, next generation sequencing and clinical profiling","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"RNA regulation and disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; Bundesministerium für Bildung und Forschung; ZonMw; Ontario Genomics Institute; Canadian Institutes of Health Research; McGill University Health Centre; Genome Canada; Ontario Genomics; Children's Hospital Foundation; American Society of Neuroradiology; McGill University","keywords":"White matter; Exome sequencing; Leukoencephalopathy; Genetic testing; Phenotype; Neuroimaging; Genetic heterogeneity; Magnetic resonance imaging","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004709322,0.0006257381,0.0005551198,0.0002334099,0.0006388868,0.0002302373,0.0003366611,0.0009481176,0.00003589693],"category_scores_gemma":[0.0005975985,0.0007110497,0.0003349705,0.0001994861,0.0000775673,0.0001214693,0.0001507061,0.0005853891,0.00002176844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002719152,"about_ca_system_score_gemma":0.0003922102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001786869,"about_ca_topic_score_gemma":0.0004128432,"domain_scores_codex":[0.9962783,0.000305812,0.001112898,0.001323053,0.0004804201,0.0004994872],"domain_scores_gemma":[0.9976816,0.0000344011,0.0007199864,0.0006872357,0.0005114044,0.0003654286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001628835,0.00003938771,0.0005284523,0.000327807,0.0001079003,0.00001638108,0.000006908633,0.0001895606,0.9149972,0.001169092,0.000006366742,0.08244808],"study_design_scores_gemma":[0.001872495,0.0004162383,0.001478235,0.0010305,0.0005416915,0.0000832703,0.001235302,0.003458787,0.9816239,0.001667839,0.004582962,0.002008735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993601,0.002121256,0.00006269436,0.000004884376,0.001208102,0.0009320202,0.0001486706,0.0001027476,0.001818588],"genre_scores_gemma":[0.9649987,0.003203704,0.02346418,0.0003032193,0.000406653,0.0000597778,0.002378388,0.0001946826,0.004990656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08043934,"threshold_uncertainty_score":0.9995341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04834810870336882,"score_gpt":0.3081657239666637,"score_spread":0.2598176152632949,"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."}}