{"id":"W2271557391","doi":"10.2196/medinform.4923","title":"Computerized Automated Quantification of Subcutaneous and Visceral Adipose Tissue From Computed Tomography Scans: Development and Validation Study","year":2016,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adipose tissue; Intraclass correlation; Automated method; Nuclear medicine; Medicine; Computed tomography; Tomography; Reliability (semiconductor); Interclass correlation; Subcutaneous adipose tissue; Biomedical engineering; Computer science; Radiology; Reproducibility; Artificial intelligence; Mathematics; Statistics; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000387089,0.0001522076,0.0003474742,0.0002023729,0.00005863301,0.00002838895,0.00009402671,0.0001266235,0.00004304433],"category_scores_gemma":[0.0000386615,0.0001063159,0.00002099715,0.0001819233,0.0001493795,0.0001590643,0.00007370183,0.00009943629,0.000005714749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003741463,"about_ca_system_score_gemma":0.00009111282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001441014,"about_ca_topic_score_gemma":0.000004191458,"domain_scores_codex":[0.9981041,0.00006975666,0.0008267371,0.00012931,0.0007308447,0.000139276],"domain_scores_gemma":[0.9990183,0.000112449,0.0002519046,0.0002101485,0.0001929759,0.0002142596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001589337,0.003791948,0.08815613,0.001604817,0.0012638,0.0001399481,0.02805486,0.000002491546,0.1607776,0.0004608172,0.004662049,0.7094961],"study_design_scores_gemma":[0.02085115,0.005072042,0.603493,0.005538536,0.0004994213,0.0005583506,0.0014504,0.07467018,0.2830345,0.0001810126,0.003558915,0.001092481],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580798,0.00003506005,0.03996977,0.0002469754,0.00005686126,0.001060304,0.00001044416,0.0004891822,0.00005161581],"genre_scores_gemma":[0.9631284,0.00001454343,0.03653021,0.0001453854,0.00002631734,0.00003848913,0.00009965827,0.00001049954,0.000006470987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7084037,"threshold_uncertainty_score":0.4335435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348247986736511,"score_gpt":0.3047822719265829,"score_spread":0.2812997920592178,"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."}}