{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0177293,0.0006478513,0.0006483034,0.001550268,0.001085863,0.005795228,0.002100399,0.001864896,0.009810137],"category_scores_gemma":[0.06063339,0.0003189986,0.001155502,0.002306961,0.002162587,0.004806652,0.004186947,0.003718547,0.002861356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002392886,"about_ca_system_score_gemma":0.005029141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005394908,"about_ca_topic_score_gemma":0.003532511,"domain_scores_codex":[0.9899504,0.006526118,0.0004387947,0.001082962,0.001681918,0.0003198251],"domain_scores_gemma":[0.9481066,0.03666579,0.001840253,0.005398837,0.005828483,0.002159972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003428374,0.000449294,0.02060376,0.001223159,0.000224085,0.0002710247,0.001515081,0.02029478,0.00132601,0.05382359,0.08398189,0.8159445],"study_design_scores_gemma":[0.0003514922,0.0009023059,0.0161099,0.003392517,0.0002570138,0.0007980551,0.003387598,0.1913911,0.005573467,0.4257287,0.351795,0.0003127572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09322251,0.01634399,0.4554571,0.3528828,0.003372752,0.0007281969,0.003914145,0.005191575,0.06888697],"genre_scores_gemma":[0.4709541,0.01191999,0.4824765,0.01744791,0.004276061,0.0006357395,0.002232159,0.0008180307,0.009239526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0177293,"threshold_uncertainty_score":0.09376264,"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."}}