{"id":"W2970203934","doi":"10.1001/jamapediatrics.2019.2740","title":"Leveraging Omics Profiling to Advance the Treatment of Pediatric Obesity","year":2019,"lang":"en","type":"article","venue":"JAMA Pediatrics","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Omics; Profiling (computer programming); Obesity; Bioinformatics; Computational biology; Data science; 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":[0.006926859,0.0008145575,0.0008787112,0.003882528,0.0004882195,0.003862567,0.0008385989,0.0009787467,0.007961882],"category_scores_gemma":[0.02659781,0.0003967998,0.001540348,0.002081163,0.0004141802,0.002184416,0.002441483,0.003494051,0.003058197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006945477,"about_ca_system_score_gemma":0.001678394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567191,"about_ca_topic_score_gemma":0.003542578,"domain_scores_codex":[0.9968655,0.001275157,0.0003258499,0.0004248206,0.000888426,0.0002202457],"domain_scores_gemma":[0.986238,0.006891494,0.001881875,0.001427016,0.002425553,0.001135965],"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":[0.0004586444,0.0002715646,0.1233469,0.0009706307,0.0005924345,0.0009933275,0.0004704437,0.001288715,0.006120007,0.006383976,0.167416,0.6916873],"study_design_scores_gemma":[0.0002707495,0.0008444434,0.1531372,0.006113215,0.001410963,0.006313751,0.001271175,0.01234644,0.01544181,0.04461626,0.7579805,0.0002535411],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1199372,0.1790361,0.2520532,0.2397867,0.01080764,0.001355729,0.0383061,0.009203197,0.1495142],"genre_scores_gemma":[0.4625476,0.1140063,0.3118303,0.05789478,0.01434423,0.0009883007,0.02047833,0.002647464,0.01526279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007961882,"threshold_uncertainty_score":0.03663319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609157698509762,"score_gpt":0.2689824032238572,"score_spread":0.2528908262387596,"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."}}