{"id":"W4387532467","doi":"10.2196/38852","title":"Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study","year":2023,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Waist; Pearson product-moment correlation coefficient; Circumference; Medicine; Tape measure; Preprint; Abdominal obesity; mHealth; Artificial intelligence; Nuclear medicine; Statistics; Medical physics; Body mass index; Computer science; Mathematics; Internal medicine; Nursing; Geometry","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":[0.001484913,0.0002204688,0.0004880281,0.0002662039,0.0002078274,0.0000860992,0.00009386263,0.00009109122,0.00003251952],"category_scores_gemma":[0.0002262957,0.00020682,0.0001286373,0.0007558918,0.00006449903,0.0001190631,0.00009679988,0.0001908731,0.00002132376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206923,"about_ca_system_score_gemma":0.0001913235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001311584,"about_ca_topic_score_gemma":0.0004058517,"domain_scores_codex":[0.9974857,0.0002169001,0.0003781847,0.0005235038,0.001063,0.0003327136],"domain_scores_gemma":[0.9988989,0.0000925643,0.0001095452,0.0004038638,0.0003245763,0.0001706009],"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":[0.000167348,0.001166701,0.09597839,0.0002528241,0.001899823,0.0001507398,0.01872197,0.0002000375,0.01800435,5.467865e-7,0.0009375685,0.8625197],"study_design_scores_gemma":[0.008045547,0.0005821145,0.923669,0.000835259,0.001203484,0.00001981928,0.008330137,0.04585409,0.007291037,0.00004749112,0.003587999,0.0005340592],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946849,0.0006388873,0.000861271,0.000077829,0.0002424996,0.002858522,0.00001223746,0.0003279069,0.0002959202],"genre_scores_gemma":[0.99582,0.000003945961,0.003212634,0.00004864167,0.0002256441,0.0004364181,0.0001496497,0.0000387282,0.00006434304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8619856,"threshold_uncertainty_score":0.8433875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05456052606290916,"score_gpt":0.3049496140766144,"score_spread":0.2503890880137052,"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."}}