{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02291935,0.001099537,0.0004335065,0.001061352,0.002673895,0.009171586,0.001116627,0.00432625,0.007736926],"category_scores_gemma":[0.007420779,0.0003481255,0.0007648319,0.0006856158,0.003526757,0.007635566,0.008337682,0.007694562,0.001484206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003653603,"about_ca_system_score_gemma":0.009666714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00124638,"about_ca_topic_score_gemma":0.003449848,"domain_scores_codex":[0.9914427,0.003270324,0.0002486202,0.0005142955,0.003395579,0.001128341],"domain_scores_gemma":[0.9931804,0.003480623,0.0004144851,0.0003414056,0.001127074,0.001455967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003578565,0.0005662221,0.001493845,0.00091573,0.00005486963,0.001868208,0.0072984,0.001917762,0.05632563,0.4050335,0.1317608,0.3924073],"study_design_scores_gemma":[0.00006096054,0.0006916735,0.001111138,0.0007526146,0.00003451846,0.000991303,0.004489449,0.001829371,0.02952681,0.08881848,0.8715922,0.0001014599],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07588116,0.07872286,0.1252668,0.482402,0.009060025,0.000706759,0.0003755947,0.0007211881,0.2268635],"genre_scores_gemma":[0.5233592,0.09825037,0.2101139,0.07073953,0.003384762,0.0007052728,0.0009497147,0.0006323387,0.09186486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02291935,"threshold_uncertainty_score":0.1212105,"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."}}