{"id":"W2322422487","doi":"10.1038/ejcn.2016.31","title":"Personalized nutrition by prediction of glycaemic responses: fact or fantasy?","year":2016,"lang":"en","type":"letter","venue":"European Journal of Clinical Nutrition","topic":"Diet and metabolism studies","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fantasy; Calorie; Medicine; Food science; Psychology; Computer science; Internal medicine; Biology; Artificial intelligence","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":[0.01295876,0.0007644956,0.002707541,0.0006342781,0.00130205,0.003118203,0.001876335,0.01832616,0.003807181],"category_scores_gemma":[0.05296719,0.0004391456,0.0008646012,0.0007135675,0.002591042,0.005316044,0.001105644,0.02691754,0.002705671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148613,"about_ca_system_score_gemma":0.001616475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782726,"about_ca_topic_score_gemma":0.002189215,"domain_scores_codex":[0.9948605,0.003030102,0.0007757351,0.0004636083,0.0007199208,0.0001501648],"domain_scores_gemma":[0.9542955,0.03818566,0.001774436,0.001086772,0.003111198,0.001546315],"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":[0.001642464,0.0004512019,0.02800077,0.0007989184,0.0006464682,0.004909746,0.0008889078,0.000541978,0.0008462764,0.02249875,0.7558867,0.1828878],"study_design_scores_gemma":[0.002189273,0.001017732,0.02258637,0.003512026,0.0007809411,0.01232837,0.003172694,0.006206409,0.001107483,0.1859891,0.7606993,0.0004102117],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001073239,0.005150877,0.0002680443,0.9865523,0.006029045,0.000004424984,0.00005389819,0.00001080827,0.0008573402],"genre_scores_gemma":[0.03061953,0.01346556,0.001973837,0.8370531,0.114699,0.00005537576,0.00008900989,0.00002707946,0.002017555],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01832616,"threshold_uncertainty_score":0.0685333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09993677750144303,"score_gpt":0.3747451039036298,"score_spread":0.2748083264021867,"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."}}