{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005962252,0.0002266948,0.0008596342,0.00007828348,0.0001594553,0.00003907828,0.0003806486,0.0001000594,0.00007435121],"category_scores_gemma":[0.0004493789,0.0001217059,0.00007601739,0.0003606282,0.000189484,0.00003737594,0.0002239041,0.0006968143,0.00007232405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007285797,"about_ca_system_score_gemma":0.0002057251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003007026,"about_ca_topic_score_gemma":0.00007250396,"domain_scores_codex":[0.998055,0.0003640514,0.0003187597,0.0003664078,0.0002710495,0.000624765],"domain_scores_gemma":[0.9987768,0.0002799792,0.000188647,0.000560778,0.00003823505,0.0001555714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001303873,0.00003070465,0.9912122,0.0009120408,0.0003624504,0.0002009908,0.003151244,0.0003013265,0.0005437905,0.0002858312,0.0007803657,0.0009151752],"study_design_scores_gemma":[0.01740941,0.00191232,0.007596977,0.00362273,0.0005423049,0.4334849,0.00639444,0.5046359,0.0008436415,0.0001846206,0.02217755,0.001195173],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889156,0.001146577,0.002723399,0.00560247,0.00005268698,0.0005126582,0.00004588081,0.0001669565,0.00083382],"genre_scores_gemma":[0.9960622,0.00006985306,0.003034821,0.0004779992,0.0001491367,0.00001955339,0.0001085201,0.00005549561,0.00002241456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9836152,"threshold_uncertainty_score":0.496302,"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."}}