{"id":"W2099398074","doi":"10.1093/jnci/dju353","title":"Translational Cancer Research: Balancing Prevention and Treatment to Combat Cancer Globally","year":2014,"lang":"en","type":"article","venue":"JNCI Journal of the National Cancer Institute","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Environmental Health Sciences; World Health Organization","keywords":"Cancer prevention; Biobank; Context (archaeology); Cancer; Translational research; Psychological intervention; Population; Medicine; Risk analysis (engineering); Environmental health; Bioinformatics; Biology; Pathology","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":[0.05313563,0.001077928,0.002205728,0.003398708,0.003180356,0.01328284,0.003089367,0.008125627,0.01860871],"category_scores_gemma":[0.04418438,0.0007157653,0.00156593,0.003329346,0.0139514,0.0131768,0.01166532,0.01461798,0.007003129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005692845,"about_ca_system_score_gemma":0.02555803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001711518,"about_ca_topic_score_gemma":0.00298307,"domain_scores_codex":[0.9760839,0.01446006,0.001134898,0.002053896,0.00470329,0.001563975],"domain_scores_gemma":[0.9565513,0.02155746,0.0024308,0.005372606,0.006106294,0.007981589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003134848,0.0006219478,0.005589663,0.004120356,0.0003970512,0.0002596113,0.001510824,0.001102268,0.003864227,0.2813721,0.2066756,0.4941729],"study_design_scores_gemma":[0.0001970344,0.0006165225,0.003239243,0.00469122,0.0002149087,0.0005698944,0.003056718,0.0009615974,0.001907394,0.3845128,0.5999396,0.00009300796],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002693733,0.1089064,0.02255593,0.8267166,0.011842,0.0002743688,0.0002340556,0.000356103,0.02642087],"genre_scores_gemma":[0.1419733,0.3340716,0.08703212,0.3616241,0.04911799,0.001380991,0.0009603709,0.0006527387,0.02318683],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.05313563,"threshold_uncertainty_score":0.2810115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07185625949196567,"score_gpt":0.3926074026086725,"score_spread":0.3207511431167068,"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."}}