{"id":"W3194890103","doi":"10.1200/cci.21.00040","title":"Cancer Informatics for Cancer Centers: Scientific Drivers for Informatics, Data Science, and Care in Pediatric, Adolescent, and Young Adult Cancer","year":2021,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Childhood Cancer Survivors' Quality of Life","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Cancer Institute; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Cancer; Informatics; Health informatics; Medicine; Political science; Internal medicine; Nursing; Public health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07034871,0.0007941539,0.0008800291,0.004146484,0.01005455,0.02821017,0.003780826,0.007489955,0.01494716],"category_scores_gemma":[0.08722848,0.0009865462,0.001195379,0.00860505,0.008487346,0.02503787,0.02051251,0.02970576,0.003248227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02284504,"about_ca_system_score_gemma":0.0850756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01462088,"about_ca_topic_score_gemma":0.03686188,"domain_scores_codex":[0.9636936,0.01759896,0.002598759,0.001834243,0.009859138,0.004415297],"domain_scores_gemma":[0.8290436,0.06215005,0.007977507,0.005428192,0.02803719,0.0673634],"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":[0.00003232498,0.00007290953,0.00479503,0.0007744061,0.00002308145,0.0001163549,0.003915713,0.0001814089,0.0003645251,0.05911066,0.8259797,0.1046339],"study_design_scores_gemma":[0.0000164683,0.0000485256,0.003291484,0.001018497,0.00001698737,0.000190941,0.00683347,0.0002211533,0.0002641077,0.01513909,0.9729035,0.00005570018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.001455532,0.01717915,0.002505461,0.9549544,0.0128386,0.0001371941,0.0003286265,0.0002327348,0.01036837],"genre_scores_gemma":[0.09632944,0.1304232,0.06011327,0.5923362,0.07038586,0.001340987,0.005245688,0.001387162,0.04243822],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.07034871,"threshold_uncertainty_score":0.372044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09999917990435994,"score_gpt":0.4298986349007167,"score_spread":0.3298994549963568,"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."}}