{"id":"W1985557809","doi":"10.1177/1534735414563603","title":"Personalized Integrative Oncology: Targeted Approaches for Optimal Outcomes","year":2014,"lang":"en","type":"article","venue":"Integrative Cancer Therapies","topic":"Complementary and Alternative Medicine Studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"National Institute on Minority Health and Health Disparities; National Cancer Institute; National Institutes of Health; Lotte and John Hecht Memorial Foundation","keywords":"Integrative medicine; Medicine; Alternative medicine; Health care; Medical education; Oncology; Political science; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003181188,0.0004260956,0.0009818539,0.0001399086,0.0002213137,0.00001836942,0.0001601463,0.00003762863,0.0008655516],"category_scores_gemma":[0.0004059959,0.0002349553,0.0003266165,0.0001537795,0.0006914902,0.0001072982,0.00004517218,0.0003061875,0.000008228283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002830249,"about_ca_system_score_gemma":0.0001412347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001275359,"about_ca_topic_score_gemma":0.0002854889,"domain_scores_codex":[0.9984293,0.0001722601,0.0003530311,0.000450807,0.0002416594,0.000352947],"domain_scores_gemma":[0.9982542,0.0008658419,0.0001806924,0.0001842138,0.0004071751,0.0001078078],"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":[0.004767328,0.0008215143,0.100818,0.0001691623,0.008291135,0.00001233429,0.1384379,0.00002206738,0.006776215,0.05358499,0.01208709,0.6742122],"study_design_scores_gemma":[0.01185041,0.007109606,0.02449667,0.0005414402,0.0005983228,0.00001230937,0.1494655,0.002221664,0.01770511,0.003440387,0.7819005,0.0006580129],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8517366,0.009271129,0.09139898,0.02998642,0.001406494,0.002977171,0.0005308248,0.0001835401,0.01250884],"genre_scores_gemma":[0.9766213,0.0003841366,0.01194253,0.001877356,0.0007206343,0.001447991,0.0001425056,0.00004145806,0.006822045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7698135,"threshold_uncertainty_score":0.9581199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1016631633550312,"score_gpt":0.3928400910375522,"score_spread":0.2911769276825211,"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."}}