{"id":"W2985818748","doi":"10.1182/blood-2019-130053","title":"Contributions to Global Hematology from Low and Middle-Income Countries: Insights from ASH 2018","year":2019,"lang":"en","type":"article","venue":"Blood","topic":"Global Health and Surgery","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Gross national income; Hematology; Medicine; Developing country; Family medicine; Political science; Internal medicine; Economic growth","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":[0.02901685,0.000815617,0.0007426395,0.01627069,0.001398367,0.008291987,0.001032239,0.001563292,0.01975362],"category_scores_gemma":[0.08955152,0.0003798224,0.000919385,0.02141752,0.001362364,0.004977894,0.005373809,0.001424003,0.003439872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004378585,"about_ca_system_score_gemma":0.0115209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002463492,"about_ca_topic_score_gemma":0.004244998,"domain_scores_codex":[0.9773673,0.01005084,0.004025708,0.001237079,0.005787642,0.001531384],"domain_scores_gemma":[0.8840903,0.05308062,0.02775577,0.003114217,0.02225974,0.009699363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007581459,0.0001201411,0.1254392,0.02488263,0.0006884804,0.001901617,0.01762947,0.0004493939,0.00119362,0.01292433,0.3017285,0.5122845],"study_design_scores_gemma":[0.0001010772,0.0001722051,0.1628478,0.02160482,0.0004654413,0.003040923,0.02007412,0.0002913783,0.0008490136,0.005170984,0.7852662,0.0001160672],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1513451,0.4685567,0.004817182,0.211988,0.009444068,0.001199147,0.02964088,0.000412812,0.1225963],"genre_scores_gemma":[0.5908462,0.3255057,0.008734188,0.03229951,0.01629787,0.001244241,0.0134664,0.0003698988,0.01123608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02901685,"threshold_uncertainty_score":0.1534576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00766220152782182,"score_gpt":0.2666413160799416,"score_spread":0.2589791145521197,"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."}}