{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002941672,0.0005710348,0.001347344,0.0005276956,0.0006299764,0.0006265746,0.0009063198,0.0003773094,0.00004778631],"category_scores_gemma":[0.002666131,0.0005391951,0.0002040033,0.001398368,0.001287938,0.002964655,0.001049893,0.0008100446,0.000003921708],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009888472,"about_ca_system_score_gemma":0.008146897,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651174,"about_ca_topic_score_gemma":0.03385222,"domain_scores_codex":[0.9927067,0.00005760944,0.003818079,0.00059859,0.001609597,0.001209471],"domain_scores_gemma":[0.9922987,0.0005331716,0.001466666,0.001363101,0.003507987,0.000830384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007471415,0.0003105073,0.8055456,0.02007827,0.0002145435,0.000003011933,0.05536315,0.0002317986,0.000008815432,0.0002034596,0.05000935,0.06728435],"study_design_scores_gemma":[0.04805409,0.001293435,0.2038189,0.01011722,0.003700069,0.00005923808,0.1580627,0.1445962,0.0007972397,0.0001515837,0.4257511,0.003598207],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9591733,0.01181056,0.001439585,0.004094304,0.008383404,0.004884006,0.009452873,0.0001553943,0.0006065911],"genre_scores_gemma":[0.6126895,0.2577173,0.06527102,0.04758194,0.007424739,0.004097608,0.003573656,0.0003766906,0.001267518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6017267,"threshold_uncertainty_score":0.999706,"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."}}