{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007033633,0.0001933836,0.0003885297,0.0001574439,0.00006616509,0.000002278135,0.00007860899,0.0003649378,0.0009252056],"category_scores_gemma":[0.0000266199,0.0001345783,0.0001086613,0.0004082372,0.0001581904,0.00005631312,0.000006096873,0.0006614148,0.0000105995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005190873,"about_ca_system_score_gemma":0.0001211648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009296245,"about_ca_topic_score_gemma":0.000006658012,"domain_scores_codex":[0.9986835,0.00003924776,0.0003491063,0.000268815,0.0004347895,0.0002245195],"domain_scores_gemma":[0.999222,0.00003021595,0.00006946928,0.0003089521,0.0001066557,0.0002626973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008686461,0.003735056,0.004246831,0.004098218,0.0007889223,0.0006001812,0.01578449,0.1524121,0.5926622,0.0003412475,0.02760498,0.1890393],"study_design_scores_gemma":[0.03674305,0.003820132,0.01598442,0.004454373,0.005447727,0.0002667111,0.002528626,0.7047489,0.109576,0.002170128,0.1125157,0.00174421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620045,0.02620547,0.00047727,0.0005211601,0.0004994638,0.0003826886,0.000005007815,0.00006566278,0.009838742],"genre_scores_gemma":[0.9898376,0.002993828,0.0002423289,0.000683567,0.0002527215,0.000003569264,0.00002877877,0.00003485906,0.005922672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5523368,"threshold_uncertainty_score":0.9999881,"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."}}