{"id":"W4297817394","doi":"10.17615/hc27-f622","title":"Machine Learning Can Unlock Insights Into Mortality","year":2022,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill; Agency for Healthcare Research and Quality; University of North Carolina at Chapel Hill; National Institutes of Health; Hamilton Health Sciences Foundation","keywords":"Computer science; Data science","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":[0.01346708,0.001296169,0.001786337,0.004862962,0.001054678,0.008178099,0.00132711,0.002481527,0.02432779],"category_scores_gemma":[0.1209472,0.000494124,0.001300712,0.004593388,0.003412909,0.01061816,0.00403045,0.01029698,0.007797891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002105997,"about_ca_system_score_gemma":0.003983416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003340448,"about_ca_topic_score_gemma":0.00396246,"domain_scores_codex":[0.994324,0.003264361,0.0003022585,0.0006292019,0.001191011,0.0002892581],"domain_scores_gemma":[0.9060038,0.07628319,0.00318341,0.006428101,0.005725156,0.002376298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006283592,0.0001498533,0.02024079,0.001755079,0.0006825905,0.000195059,0.0006169404,0.005787543,0.0006152118,0.2009246,0.1960597,0.5723443],"study_design_scores_gemma":[0.00005836596,0.0001413929,0.008226068,0.00218497,0.0002370615,0.0001494928,0.000430981,0.008803808,0.0006566861,0.8470555,0.1319424,0.0001133623],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01648939,0.1139695,0.1343363,0.6285464,0.02262218,0.00009624405,0.01044552,0.00183385,0.07166065],"genre_scores_gemma":[0.476804,0.2366003,0.06426211,0.1175168,0.06757349,0.0002950867,0.008506891,0.001644275,0.02679702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02432779,"threshold_uncertainty_score":0.08138454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192247443258044,"score_gpt":0.2543086995440863,"score_spread":0.2323862251115059,"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."}}