{"id":"W2272470775","doi":"10.1016/j.molmed.2016.01.007","title":"Exon Snipping in Duchenne Muscular Dystrophy","year":2016,"lang":"en","type":"letter","venue":"Trends in Molecular Medicine","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Duchenne muscular dystrophy; Dystrophin; Exon; CRISPR; Exon skipping; Muscular dystrophy; Limiting; Genome editing; Gene; Genetics; Medicine; Bioinformatics; Biology; Alternative splicing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003269636,0.0005402984,0.0006292654,0.0007716929,0.00002042325,0.00001008034,0.0004425764,0.0009260701,0.0001735349],"category_scores_gemma":[0.0001149221,0.0004535522,0.0001759765,0.0004046101,0.000144382,0.000002816903,0.0001455922,0.0009068787,0.0000104446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007289824,"about_ca_system_score_gemma":0.00003932127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009040073,"about_ca_topic_score_gemma":0.00004746244,"domain_scores_codex":[0.9972637,0.0001247071,0.0005886646,0.0008804405,0.0003867855,0.0007557193],"domain_scores_gemma":[0.9988245,0.00002039108,0.0001153904,0.0009067898,0.00004703873,0.00008589769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003752339,0.00002717381,0.0001951756,0.0001847497,0.0001160731,0.003273554,0.00009101848,0.0002674619,0.7755173,0.00001418233,0.1920429,0.02823284],"study_design_scores_gemma":[0.002601448,0.0003433698,0.0009209188,0.0008089524,0.00006460742,0.00008443704,0.00002139387,0.00005140493,0.03038253,0.00006848168,0.9638978,0.0007546275],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1316162,0.03704216,0.046695,0.7643706,0.003486705,0.001031969,0.0001039314,0.0001433693,0.01551009],"genre_scores_gemma":[0.5506946,0.003220312,0.003537975,0.4016571,0.02247601,0.0004239691,0.005142509,0.0008353955,0.01201205],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.7718549,"threshold_uncertainty_score":0.9997916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009542288190661549,"score_gpt":0.3012511888492293,"score_spread":0.2917089006585677,"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."}}