{"id":"W4211256599","doi":"10.1002/ajh.26492","title":"A predictive algorithm for identifying children with sickle cell anemia among children admitted to hospital with severe anemia in Africa","year":2022,"lang":"en","type":"article","venue":"American Journal of Hematology","topic":"Hemoglobinopathies and Related Disorders","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; National Institutes of Health; Department for International Development; National Institute for Health and Care Research; Medical Research Council Canada; University College London; Wellcome Trust","keywords":"Medicine; Anemia; Sickle cell anemia; Pediatrics; Malaria; Blood transfusion; Algorithm; Internal medicine; Disease; Immunology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003459001,0.001108187,0.001433086,0.003505972,0.0006986194,0.001588264,0.001747639,0.001013703,0.002917889],"category_scores_gemma":[0.01768837,0.0004000367,0.001051817,0.001412105,0.000293031,0.0008239641,0.001251717,0.001543574,0.0007481973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008088537,"about_ca_system_score_gemma":0.001567107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006319572,"about_ca_topic_score_gemma":0.005212834,"domain_scores_codex":[0.998317,0.0006356708,0.0002768866,0.0002899164,0.0003016821,0.0001787687],"domain_scores_gemma":[0.9946123,0.003024878,0.0006947891,0.0001263046,0.00120782,0.0003338698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001756675,0.0006799148,0.7240756,0.0003188394,0.000462529,0.0008040715,0.0003980279,0.03513636,0.001603423,0.001605932,0.01878512,0.2143735],"study_design_scores_gemma":[0.0007914525,0.0008950474,0.1363407,0.0005108214,0.0006899147,0.002383444,0.0007003006,0.838628,0.003256292,0.007444211,0.008215677,0.0001441276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6923014,0.004019642,0.2723485,0.006935714,0.0005514298,0.003455631,0.008501195,0.002681528,0.009204922],"genre_scores_gemma":[0.7720659,0.0007660422,0.2175131,0.0007922999,0.0001941427,0.001504992,0.005819117,0.00006108999,0.001283342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006319572,"threshold_uncertainty_score":0.0182932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003990007576450975,"score_gpt":0.218237962249873,"score_spread":0.214247954673422,"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."}}