{"id":"W2498530809","doi":"10.1177/1093526617698603","title":"Advancing Clinicopathologic Diagnosis of High-risk Neuroblastoma Using Computerized Image Analysis and Proteomic Profiling","year":2017,"lang":"en","type":"article","venue":"Pediatric and Developmental Pathology","topic":"Neuroblastoma Research and Treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Center for Research Resources; National Institutes of Health; National Cancer Institute; Bear Necessities Pediatric Cancer Foundation; Rally Foundation","keywords":"Medicine; Risk stratification; Neuroblastoma; Profiling (computer programming); Pathology; Bioinformatics; Oncology; Computational biology; Computer science; Internal medicine; Biology","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":[0.0003251055,0.0002117139,0.0006542578,0.000390219,0.0003906578,0.00004086563,0.000104067,0.00009585609,0.0000136099],"category_scores_gemma":[0.0004670008,0.0001774162,0.00008797597,0.0002038829,0.0002815836,0.0001539973,0.0004015527,0.000212427,0.000005489523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003530537,"about_ca_system_score_gemma":0.0001117733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009032831,"about_ca_topic_score_gemma":0.00001432181,"domain_scores_codex":[0.9984255,0.0001041247,0.0004165723,0.0005200656,0.0001694025,0.0003643713],"domain_scores_gemma":[0.998927,0.0001448489,0.0003982718,0.000252868,0.00007302237,0.0002040024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002698382,0.0001467003,0.9846521,0.0001105498,0.0001228771,0.0009697581,0.00005836599,0.000001463484,0.009301862,0.000004444511,0.000003856299,0.004358158],"study_design_scores_gemma":[0.002737333,0.0006117408,0.9833457,0.00001384986,0.0009091884,0.0002993216,0.00003289944,0.0004160946,0.01140631,0.00008401197,0.000001407405,0.0001421221],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979822,0.0001744864,0.001057435,0.00005073223,0.00007388628,0.0005556726,0.0000405877,0.00003090802,0.00003405937],"genre_scores_gemma":[0.8959028,0.001655954,0.1022633,0.00003760841,0.00005865767,0.00004158157,0.00001408217,0.0000168241,0.000009218215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1020795,"threshold_uncertainty_score":0.7234823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780692742814989,"score_gpt":0.3050325928743012,"score_spread":0.2872256654461513,"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."}}