{"id":"W1504869454","doi":"10.1038/72279","title":"Genetic correction of sickle cell disease: Insights using transgenic mouse models","year":2000,"lang":"en","type":"article","venue":"Nature Medicine","topic":"Hemoglobinopathies and Related Disorders","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"","keywords":"Fetal hemoglobin; In vivo; Transgene; Disease; Hemoglobin; Biology; Fetus; Genetically modified mouse; Genetic enhancement; Cell; Hemoglobin s; Sickle cell anemia; Immunology; Hemoglobinopathy; Gene; Hemolytic anemia; Medicine; Genetics; Pathology; Pregnancy; Biochemistry","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":[0.0005762802,0.000604188,0.0004092424,0.0009364853,0.0002247391,0.0005386852,0.0005126184,0.0007019861,0.001064627],"category_scores_gemma":[0.0003869151,0.0002699606,0.0002900911,0.0002564667,0.0006249761,0.0005270422,0.0002627186,0.001390748,0.0002310733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000324093,"about_ca_system_score_gemma":0.0003211434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005540304,"about_ca_topic_score_gemma":0.0008560373,"domain_scores_codex":[0.999792,0.00004064511,0.00002566757,0.00003559819,0.00008146324,0.00002462705],"domain_scores_gemma":[0.9997641,0.00007635848,0.00006918957,0.00002613912,0.00002676797,0.00003727439],"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":[0.0001544744,0.00009411507,0.000296014,0.0001909765,0.0000284624,0.000462936,0.00006257559,0.0004657237,0.9757904,0.007250852,0.00105212,0.01415129],"study_design_scores_gemma":[0.0002281613,0.0008184614,0.003805134,0.0001768596,0.0002612656,0.004872859,0.0001522496,0.005879236,0.9079493,0.00841383,0.06738828,0.00005432097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7132064,0.05893403,0.1950608,0.01081667,0.001662166,0.0002717995,0.002069011,0.002662467,0.01531674],"genre_scores_gemma":[0.8532062,0.08781114,0.0486979,0.001127565,0.000347479,0.0001689324,0.001070289,0.0002416176,0.007328936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001064627,"threshold_uncertainty_score":0.003561556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007902283139637244,"score_gpt":0.2398093257055137,"score_spread":0.2319070425658765,"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."}}