{"id":"W4389243360","doi":"10.1182/blood-2023-187754","title":"Low-Cost Automated Microscopy and Morphology-Based Machine Learning Classification of Sickle Cell Disease and Beta-Thalassemia in Nepal and Canada","year":2023,"lang":"en","type":"article","venue":"Blood","topic":"Hemoglobinopathies and Related Disorders","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; St. Paul's Hospital; University of British Columbia","funders":"","keywords":"Medicine; Sickle cell trait; Thalassemia; Pediatrics; Malaria; Alpha-thalassemia; Asymptomatic; Gold standard (test); Disease; Internal medicine; Pathology; Genotype; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007488728,0.0002814844,0.0002341097,0.001430135,0.0008841067,0.0009830934,0.0009824057,0.0006266336,0.0024835],"category_scores_gemma":[0.002651309,0.0002313751,0.0003182637,0.0008270236,0.0004959187,0.0003907214,0.0008746015,0.0004187524,0.0003068823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002867183,"about_ca_system_score_gemma":0.004167334,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3646218,"about_ca_topic_score_gemma":0.5185404,"domain_scores_codex":[0.9994645,0.0001116403,0.00003726237,0.0001182247,0.000153866,0.0001146428],"domain_scores_gemma":[0.9983351,0.0003345708,0.000180569,0.00007846093,0.0009482602,0.0001231007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007095492,0.0005042421,0.7994757,0.0008441604,0.0002724488,0.003073825,0.001806399,0.004046085,0.005581601,0.001716314,0.01230477,0.1696648],"study_design_scores_gemma":[0.0001187743,0.0004006051,0.9436145,0.000427804,0.0002206045,0.004379328,0.003771275,0.02335381,0.003679456,0.001077972,0.01886163,0.00009432993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977359,0.001720352,0.002845457,0.001220991,0.00002940429,0.0004092127,0.00597196,0.00009129712,0.01035236],"genre_scores_gemma":[0.9859558,0.001058418,0.00504457,0.0004285586,0.00001693523,0.0002234401,0.003902648,0.00001344919,0.003356104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6353782,"threshold_uncertainty_score":0.724999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00672324464941715,"score_gpt":0.2316216232669214,"score_spread":0.2248983786175042,"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."}}