{"id":"W4249812721","doi":"10.31525/ct1-nct03988894","title":"mDASHNa-CC APP to Support a Healthy Diet and Hypertension Control for Chinese Canadian Seniors","year":2019,"lang":"en","type":"article","venue":"Case Medical Research","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Gerontology; Control (management); Medicine; Psychology; Computer science; Artificial intelligence","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":[0.001197175,0.0003423581,0.0003351648,0.000802407,0.002457692,0.001081745,0.001207788,0.001889059,0.0931779],"category_scores_gemma":[0.008452885,0.0001703231,0.0005928012,0.0004729019,0.0004007595,0.0004091961,0.001782136,0.001355606,0.0125183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006867318,"about_ca_system_score_gemma":0.03742498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5047017,"about_ca_topic_score_gemma":0.7947049,"domain_scores_codex":[0.9991276,0.0001224618,0.00003406123,0.00005791181,0.0003153712,0.0003426581],"domain_scores_gemma":[0.9898239,0.0007429397,0.0002762246,0.0001890879,0.002807216,0.006160617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000310648,0.0004016583,0.007878478,0.00008486416,0.00002414559,0.0003742423,0.0001943644,0.00004433182,0.0003750167,0.0004388566,0.9023241,0.08754914],"study_design_scores_gemma":[0.001409841,0.0004629633,0.1059484,0.001047737,0.0001273266,0.0004894795,0.00142074,0.0004108859,0.0004171428,0.001255067,0.8869523,0.00005806676],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07140674,0.006384361,0.001102961,0.3781106,0.01879768,0.001816657,0.01530095,0.002387013,0.5046931],"genre_scores_gemma":[0.308438,0.009435524,0.01172818,0.1482594,0.01559207,0.002842988,0.01219071,0.0005907604,0.4909223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4952983,"threshold_uncertainty_score":0.9964303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.023624622045248,"score_gpt":0.3600427829329949,"score_spread":0.3364181608877469,"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."}}