{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004506161,0.0009122786,0.0005183235,0.0009865316,0.0001845328,0.0004465073,0.0007743941,0.000539419,0.0009580384],"category_scores_gemma":[0.009148925,0.0003225518,0.0006786023,0.0003961207,0.0005132782,0.0006838753,0.000638613,0.0003446445,0.000427086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004102564,"about_ca_system_score_gemma":0.0007769991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001310303,"about_ca_topic_score_gemma":0.001154298,"domain_scores_codex":[0.9980522,0.0008926278,0.0001773195,0.0002332277,0.0005314795,0.000113169],"domain_scores_gemma":[0.9946983,0.002390489,0.0004934146,0.0003768793,0.001613564,0.0004273821],"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":[0.007689816,0.01783076,0.5077192,0.001602628,0.0006849702,0.002066563,0.00351154,0.003901321,0.04009367,0.0004786875,0.002199855,0.412221],"study_design_scores_gemma":[0.002088289,0.1399457,0.7608451,0.0004398666,0.001586433,0.005671531,0.001816435,0.03778898,0.04220041,0.0002747344,0.007142744,0.000199922],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926735,0.0001587447,0.004807865,0.00003469457,0.00001500157,0.001585204,0.0001728311,0.00009684941,0.000455334],"genre_scores_gemma":[0.9622812,0.0004809978,0.03229663,0.0001013303,0.00003398938,0.002653813,0.001105802,0.00004540849,0.001000858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004506161,"threshold_uncertainty_score":0.02383113,"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."}}