{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008587568,0.001084021,0.0007549727,0.002533993,0.0003801497,0.0008776418,0.001790993,0.001189107,0.001177082],"category_scores_gemma":[0.01423404,0.0006336418,0.000884188,0.001408172,0.001282921,0.000787681,0.001044144,0.0005578182,0.000606856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008028846,"about_ca_system_score_gemma":0.001043778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002968271,"about_ca_topic_score_gemma":0.002072127,"domain_scores_codex":[0.9951566,0.001677023,0.0003644469,0.0009583491,0.001685423,0.0001581787],"domain_scores_gemma":[0.9848605,0.005651006,0.001266585,0.001781409,0.006109011,0.0003314076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004565933,0.006172018,0.4241849,0.001118576,0.00134656,0.0008399833,0.001525106,0.02940851,0.180678,0.0009946384,0.001786117,0.3473797],"study_design_scores_gemma":[0.0009747167,0.01518982,0.6714491,0.0001968006,0.001181607,0.005358774,0.0006312689,0.2136391,0.08485409,0.0005738214,0.005652712,0.0002982142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.940092,0.001026712,0.05604928,0.00003821153,0.0000400928,0.0008690022,0.000508322,0.0003988771,0.0009774042],"genre_scores_gemma":[0.9090745,0.0005469404,0.0868753,0.00007638123,0.0000424055,0.0007850774,0.001810276,0.0001139793,0.0006751462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008587568,"threshold_uncertainty_score":0.04541594,"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."}}