{"id":"W3036667822","doi":"10.1007/s40778-020-00173-3","title":"In Vivo Genome Engineering for the Treatment of Muscular Dystrophies","year":2020,"lang":"en","type":"article","venue":"Current Stem Cell Reports","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"","keywords":"CRISPR; Cas9; Genome editing; Muscular dystrophy; Biology; Genetic enhancement; Phenotype; Computational biology; Muscle disorder; Gene; Genetics; Bioinformatics; Medicine; Internal medicine","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":[0.00004381134,0.0001096127,0.0001287369,0.00001866762,0.00001578958,0.000005435953,0.00005393518,0.00003439812,0.000005366094],"category_scores_gemma":[0.000006239988,0.00008327202,0.000110751,0.00005296094,0.00001063012,0.00000125789,0.00001941731,0.00002293063,3.137836e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001046023,"about_ca_system_score_gemma":0.00001727869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002468354,"about_ca_topic_score_gemma":0.000001039595,"domain_scores_codex":[0.9993744,0.000004338562,0.000228543,0.0001985329,0.0000614027,0.000132713],"domain_scores_gemma":[0.9996589,0.00001010138,0.00006016964,0.0002044648,0.00002141772,0.00004496539],"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":[0.00001712212,0.00007419196,0.0005831118,0.0002054197,0.00003515335,0.00001622062,0.0003352941,0.03709998,0.9595026,0.000004607177,0.0001695446,0.0019568],"study_design_scores_gemma":[0.0002590862,0.0002559764,0.0003837464,0.000008363418,0.0000279647,0.000008551755,0.00005150215,0.001400943,0.6488116,0.000001232271,0.3486849,0.0001061538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9434155,0.02639868,0.02904375,0.00007410967,0.0005024114,0.0005080267,0.00001282144,0.000009081971,0.00003567708],"genre_scores_gemma":[0.9981412,0.001330016,0.0001509155,0.000006737514,0.0002164596,0.00006645133,0.00001209585,0.00001537761,0.00006080341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3485153,"threshold_uncertainty_score":0.3395734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370532217596285,"score_gpt":0.2678671630534543,"score_spread":0.2541618408774915,"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."}}