{"id":"W2913101658","doi":"10.1109/dsaa.2018.00027","title":"Cohort Representation and Exploration","year":2018,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Representation (politics); Cohort; Trajectory; Similarity (geometry); Data mining; Machine learning; Artificial intelligence; Information retrieval; Statistics; Mathematics","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":[0.0001449876,0.00002887341,0.0000327206,0.00003093556,0.00007743739,0.00006308677,0.0001039346,0.00001615332,0.00002681983],"category_scores_gemma":[0.00006004776,0.00002539894,0.00000487435,0.0001250936,0.00002045574,0.0004518226,0.00006818002,0.00003466749,0.00007189681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006938144,"about_ca_system_score_gemma":0.00001027113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001956552,"about_ca_topic_score_gemma":0.00003787752,"domain_scores_codex":[0.9995573,0.0000455501,0.00006853305,0.0001709494,0.00009346028,0.00006416472],"domain_scores_gemma":[0.9996367,0.00002911842,0.00002291888,0.0002146822,0.00006452732,0.00003206612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003426792,0.0000102958,0.5109085,0.0000117714,0.000005408788,0.000002993098,0.002856442,0.00002316716,0.0002982248,0.2847241,0.00411484,0.1970408],"study_design_scores_gemma":[0.0001477891,0.0001777543,0.4891061,0.000007298652,0.0000017745,0.00002110307,0.00006797201,0.4843871,0.002169056,0.01824376,0.005543957,0.0001262534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08007444,0.00001113302,0.8979773,0.005213288,0.0002349846,0.000104092,5.310317e-8,0.0001896753,0.01619506],"genre_scores_gemma":[0.9467894,0.000005209464,0.0520888,0.0004069181,0.00009210925,0.000006808756,0.00000103955,0.000001945474,0.0006077046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.866715,"threshold_uncertainty_score":0.1035739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04059641533738914,"score_gpt":0.3446512238114328,"score_spread":0.3040548084740436,"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."}}