{"id":"W2124082826","doi":"10.1136/archdischild-2013-305163","title":"Identifying the best body mass index metric to assess adiposity change in children","year":2014,"lang":"en","type":"article","venue":"Archives of Disease in Childhood","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; McGill University","funders":"Medical Research Council; Canadian Institutes of Health Research","keywords":"Medicine; Body mass index; Metric (unit); Index (typography); Body volume index; Pediatrics; Fat mass; Internal medicine; Classification of obesity; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002034679,0.0006110272,0.0004667831,0.001039195,0.0003389516,0.0008757539,0.0004640655,0.000541527,0.0008220902],"category_scores_gemma":[0.006706724,0.0001231401,0.0003453656,0.001622992,0.0003124484,0.0005263715,0.0005254263,0.0005977645,0.000383883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001587713,"about_ca_system_score_gemma":0.001352339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07647276,"about_ca_topic_score_gemma":0.1695505,"domain_scores_codex":[0.9992585,0.0001813403,0.00007320372,0.0001654729,0.0002281937,0.00009325399],"domain_scores_gemma":[0.9972417,0.0004033278,0.0009876656,0.00008952191,0.001028891,0.0002487991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000548091,0.00001097168,0.9911869,0.00003609384,0.00003206764,0.00001506105,0.00006003309,0.0001071317,0.0001781414,0.00001994498,0.0005519153,0.007746938],"study_design_scores_gemma":[0.000003437818,0.00005960034,0.9984627,0.00003891587,0.00001679803,0.0000518247,0.0001547502,0.0004520673,0.0001657652,0.00002392597,0.0005656319,0.000004602924],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855898,0.004132518,0.003047992,0.0006019462,0.00004923605,0.00009582727,0.00408582,0.00008480091,0.002311954],"genre_scores_gemma":[0.9884178,0.000794373,0.007547537,0.0001036657,0.00002332364,0.00007433312,0.002480341,0.00002175971,0.0005368133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07647276,"threshold_uncertainty_score":0.1520553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03468769334445827,"score_gpt":0.3091586933091864,"score_spread":0.2744709999647281,"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."}}