{"id":"W3047477991","doi":"10.1158/1538-7445.pedca19-a63","title":"Abstract A63: Overcoming challenges in health care with machine learning: Innovation from retinoblastoma","year":2020,"lang":"en","type":"article","venue":"Cancer Research","topic":"Retinopathy of Prematurity Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Esri (Canada); Hospital for Sick Children","funders":"","keywords":"Retinoblastoma; Timeline; Medicine; Health care; Electronic health record; Medical physics; Disease; Family medicine; Artificial intelligence; Computer science; Pathology; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006810183,0.0001416992,0.000377759,0.0002368208,0.0001479698,0.00002419901,0.0001281663,0.00007629497,0.000105457],"category_scores_gemma":[0.0005611082,0.0001188662,0.00002414374,0.0007344239,0.0001197818,0.00007729041,0.0001140519,0.001397675,0.00002424002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004545336,"about_ca_system_score_gemma":0.0004226676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002808292,"about_ca_topic_score_gemma":0.002086091,"domain_scores_codex":[0.9978248,0.0001675755,0.0003116661,0.0004647846,0.0007895806,0.0004415271],"domain_scores_gemma":[0.9989786,0.0001860885,0.00009667817,0.000200554,0.0003962282,0.0001418738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009464692,0.000301585,0.5071573,0.01215129,0.0003524181,0.0008427196,0.2100783,0.0002279524,0.05278661,0.0007522047,0.004999577,0.2008854],"study_design_scores_gemma":[0.005704272,0.003254145,0.8614032,0.005205446,0.00001779633,0.00001093144,0.04037493,0.0002639405,0.01437731,0.00009897114,0.06883849,0.0004505921],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8994063,0.03564539,0.000003578327,0.06171602,0.00002885434,0.0005445798,0.00001723301,0.00006664905,0.0025714],"genre_scores_gemma":[0.99328,0.005518145,0.0003067957,0.0003765944,0.0003109831,0.00006760412,0.00004434087,0.00003519083,0.00006037517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3542459,"threshold_uncertainty_score":0.6072281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.187928756594559,"score_gpt":0.4201193078001524,"score_spread":0.2321905512055935,"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."}}