{"id":"W4393709172","doi":"10.5281/zenodo.2558452","title":"netDx: Interpretable patient classification using integrated patient similarity networks","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Similarity (geometry); Artificial intelligence; Computer science; Pattern recognition (psychology)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001165061,0.0005519078,0.0005381595,0.000238536,0.0002199757,0.0004889934,0.002029172,0.0007387956,0.02552812],"category_scores_gemma":[0.001162907,0.0005286862,0.0001695883,0.0006075952,0.000009925623,0.0004120136,0.001471345,0.002030186,0.00191574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005210489,"about_ca_system_score_gemma":0.0005168275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004647169,"about_ca_topic_score_gemma":0.00005549628,"domain_scores_codex":[0.9963514,0.0005018009,0.000731797,0.001130137,0.0006197103,0.000665164],"domain_scores_gemma":[0.9957098,0.0003009584,0.0009505812,0.002311161,0.0005069961,0.0002204578],"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":[0.000006317716,0.00004676865,0.00001187792,0.0003886124,0.00001964473,0.0000190313,0.00007342995,0.01243868,4.169588e-7,0.000003093141,0.977751,0.009241145],"study_design_scores_gemma":[0.00004935329,0.0001149402,0.00005523367,0.002087292,0.00000745694,0.00001061153,0.000008320336,0.4123331,0.000001604797,0.000003083382,0.5850136,0.0003153444],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000009479897,0.0004268907,0.00129532,0.00008405687,0.001211424,0.0009029683,0.9957973,0.0001882625,0.00008427446],"genre_scores_gemma":[0.0008027986,0.000009751886,0.001222125,0.001047772,0.0001523935,0.0001601956,0.996547,0.00003786831,0.00002010841],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3998945,"threshold_uncertainty_score":0.9997165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0749495723626624,"score_gpt":0.3130288945068714,"score_spread":0.238079322144209,"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."}}