{"id":"W3173143966","doi":"10.1186/s12967-021-02929-9","title":"The appropriate division of data in training of computers to predict physicians’ decision on blood transfusions: a reply to Dr. Sander de Bruyne","year":2021,"lang":"en","type":"article","venue":"Journal of Translational Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sander; Division (mathematics); Training (meteorology); Computer science; Operations research; Medicine; Medical education; Artificial intelligence; Mathematics; Engineering; Physics; Arithmetic","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":[0.02524163,0.001219479,0.002540692,0.001534065,0.002912045,0.004712349,0.003700466,0.02806366,0.004194579],"category_scores_gemma":[0.1815443,0.001132857,0.001574008,0.001629037,0.007380168,0.00780436,0.00210196,0.06585274,0.005074601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003038476,"about_ca_system_score_gemma":0.005443045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007538799,"about_ca_topic_score_gemma":0.009688526,"domain_scores_codex":[0.9851612,0.006796571,0.003010434,0.001651289,0.002950017,0.0004305492],"domain_scores_gemma":[0.7608048,0.1899159,0.004135835,0.004256454,0.03595362,0.004933439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001157626,0.00002583438,0.000527393,0.0001788951,0.00004635085,0.0001420152,0.0002293589,0.0001412773,0.0001164408,0.002155768,0.977493,0.01882798],"study_design_scores_gemma":[0.0002632065,0.0001933669,0.003219875,0.002375514,0.0001338759,0.001977216,0.0016075,0.001993323,0.0009992246,0.03325557,0.9536329,0.0003483437],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001034283,0.002518243,0.0003515153,0.984508,0.01229477,0.000005335162,0.00004270677,0.00002089081,0.000155119],"genre_scores_gemma":[0.003282676,0.004192317,0.002021496,0.9440005,0.04533856,0.00004823101,0.00005391529,0.00005806203,0.001004175],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02806366,"threshold_uncertainty_score":0.1334921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06223679924549433,"score_gpt":0.3605038584906065,"score_spread":0.2982670592451122,"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."}}