{"id":"W4411582013","doi":"10.1016/j.jcyt.2025.06.010","title":"Advancing gene-editing platforms to improve the viability of rare-disease therapeutics: key insights from a 2024 Scientific Exchange hosted by ARM, ISCT, and Danaher","year":2025,"lang":"en","type":"article","venue":"Cytotherapy","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Society for Cellular Therapy","funders":"National Institute of Allergy and Infectious Diseases; U.S. Food and Drug Administration; National Institutes of Health; Institut des Sciences du Cerveau de Toulouse; CSL Behring; National Brain Tumor Society","keywords":"Genome editing; Key (lock); Computational biology; Rare disease; Gene; Disease; Medicine; Biology; Computer science; CRISPR; Genetics; Pathology; Operating system","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":[0.0001476203,0.0001660757,0.0001509907,0.00005135454,0.0001103274,0.00005298693,0.0001918889,0.00007478147,0.00002390953],"category_scores_gemma":[0.00003476921,0.0001220602,0.00006411134,0.0001629668,0.0001085015,0.000006362347,0.00008989617,0.00007632488,0.000001178222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001652973,"about_ca_system_score_gemma":0.00005448663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002840568,"about_ca_topic_score_gemma":0.00007280377,"domain_scores_codex":[0.9990314,0.00002171979,0.0002053335,0.0004217217,0.0001127088,0.000207131],"domain_scores_gemma":[0.9992663,0.00003287559,0.00004937853,0.0004920965,0.00006956675,0.00008981847],"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":[0.00009407635,0.00004031304,0.001226377,0.00003616064,0.00007987966,5.911585e-7,0.0005603474,0.00006635132,0.9842471,0.00001183795,0.000919777,0.01271712],"study_design_scores_gemma":[0.0006416498,0.0001154991,0.003165857,0.00003723263,0.00003235447,3.882757e-7,0.0002809804,0.0007703768,0.9227567,0.0004061945,0.07161329,0.0001794397],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703459,0.01340831,0.01486441,0.000256883,0.000446968,0.0004147214,0.0001187692,0.00001317189,0.0001308368],"genre_scores_gemma":[0.9973335,0.0002246804,0.0005078566,0.0007217536,0.0001509627,0.0000497726,0.00005803197,0.00002214516,0.0009312501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07069352,"threshold_uncertainty_score":0.497747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005161849209960979,"score_gpt":0.2685198984983852,"score_spread":0.2633580492884242,"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."}}