{"id":"W4402267826","doi":"10.1158/1538-7445.pediatric24-b071","title":"Abstract B071: Automated extraction and provision of electronic health record data from children with cancer to National Childhood Cancer Center (NCCR) cancer registries","year":2024,"lang":"en","type":"article","venue":"Cancer Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cancer; Childhood cancer; Center (category theory); Medicine; Electronic health record; Family medicine; Environmental health; Health care; Internal medicine; Political science; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01675263,0.001218011,0.0008671936,0.007858104,0.0008781629,0.003362623,0.001599939,0.0004273906,0.01089998],"category_scores_gemma":[0.06175316,0.001166059,0.001230469,0.006993629,0.0004159576,0.002045689,0.003652858,0.001011512,0.009273781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001632363,"about_ca_system_score_gemma":0.00702434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.010915,"about_ca_topic_score_gemma":0.01046519,"domain_scores_codex":[0.9868439,0.004232971,0.002647622,0.002927652,0.002861209,0.0004866677],"domain_scores_gemma":[0.9407665,0.02626996,0.006800304,0.01154499,0.01337903,0.001239117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00122769,0.000658882,0.150018,0.002538515,0.0005591208,0.0009385996,0.004809011,0.006922691,0.01326148,0.004809236,0.3043222,0.5099345],"study_design_scores_gemma":[0.001050888,0.001075789,0.3228647,0.001589034,0.0005750651,0.001196864,0.004308557,0.1169677,0.08526354,0.004945545,0.459734,0.0004282011],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1638694,0.0006260704,0.1952013,0.002882968,0.0003800943,0.007881789,0.4402069,0.1701659,0.01878564],"genre_scores_gemma":[0.1485223,0.0003638848,0.4510539,0.0003866167,0.0001501451,0.004185982,0.3843962,0.005879357,0.005061693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01675263,"threshold_uncertainty_score":0.08859742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1373791065872365,"score_gpt":0.5613914591780658,"score_spread":0.4240123525908293,"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."}}