{"id":"W4393752532","doi":"10.5281/zenodo.2558451","title":"netDx: Interpretable patient classification using integrated patient similarity networks","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","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","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007751898,0.0004079685,0.0004018888,0.0004588953,0.001853056,0.00194159,0.003448866,0.0003613136,0.003712307],"category_scores_gemma":[0.0009292275,0.0004231352,0.0001106397,0.001039246,0.0001150057,0.0004991367,0.004312097,0.001728825,0.003219564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007517732,"about_ca_system_score_gemma":0.00003397436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003138605,"about_ca_topic_score_gemma":0.00000170411,"domain_scores_codex":[0.9951375,0.00152963,0.0007022467,0.00114543,0.0007781555,0.000707063],"domain_scores_gemma":[0.9957248,0.00007732648,0.0006450588,0.002092698,0.001171033,0.0002890753],"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":[0.00002749995,0.0001168256,0.000001665744,0.0001485854,0.00003561702,0.00001422233,0.0003307272,0.006033902,0.00002453216,0.0002083221,0.9254383,0.06761981],"study_design_scores_gemma":[0.0001312321,0.0003558525,0.00002962073,0.0001538777,0.00001513275,0.00006621363,0.00006181274,0.236872,0.000004830264,0.0000164377,0.7619857,0.0003073877],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005152649,0.0003377096,0.2316693,0.0007893121,0.002133011,0.002309729,0.756941,0.001622166,0.003682503],"genre_scores_gemma":[0.01056521,0.0001207449,0.001723853,0.0005680545,0.0001555382,1.680761e-7,0.9858887,0.0009064426,0.00007134815],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2308381,"threshold_uncertainty_score":0.999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05892731224706218,"score_gpt":0.2859709228848745,"score_spread":0.2270436106378123,"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."}}