{"id":"W3045804302","doi":"10.1016/s2665-9913(20)30217-4","title":"Making a big impact with small datasets using machine-learning approaches","year":2020,"lang":"en","type":"article","venue":"The Lancet Rheumatology","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Lupus Foundation of America","keywords":"Machine learning; Artificial intelligence; Medicine; Disease; Immune system; Logistic regression; Linear discriminant analysis; Random forest; Internal medicine; Computer science; Immunology","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":[0.06389886,0.004065318,0.004637404,0.009423025,0.002829953,0.0157368,0.006745453,0.005175584,0.008854132],"category_scores_gemma":[0.2853832,0.002409331,0.003796019,0.01088993,0.006093168,0.03001442,0.01382471,0.01409056,0.006039375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002226779,"about_ca_system_score_gemma":0.004140552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002629233,"about_ca_topic_score_gemma":0.002974296,"domain_scores_codex":[0.9503249,0.02879329,0.002940116,0.007578691,0.009692929,0.0006700972],"domain_scores_gemma":[0.6002347,0.3249977,0.006986876,0.05174414,0.01205489,0.003981649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001946087,0.001006697,0.03905568,0.005601579,0.006570507,0.001811708,0.001756855,0.06264187,0.006280286,0.1134973,0.1764078,0.5834236],"study_design_scores_gemma":[0.0003121403,0.0002548135,0.006119774,0.001232166,0.0005328668,0.0003365122,0.0009883791,0.143601,0.002395172,0.7659132,0.07810724,0.0002067137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.026745,0.02153192,0.8044199,0.09625663,0.008608644,0.001804495,0.01366878,0.0090297,0.01793492],"genre_scores_gemma":[0.1755982,0.01063856,0.7613571,0.0190959,0.008423748,0.002504307,0.0170916,0.002264037,0.003026513],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06389886,"threshold_uncertainty_score":0.3379335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2841654152793832,"score_gpt":0.3616368716226651,"score_spread":0.0774714563432819,"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."}}