{"id":"W2782633655","doi":"10.1016/j.bone.2018.01.013","title":"Practical considerations for obtaining high quality quantitative computed tomography data of the skeletal system","year":2018,"lang":"en","type":"review","venue":"Bone","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Alberta Bone and Joint Health Institute","funders":"","keywords":"Computer science; Standardization; Data mining; Reliability (semiconductor); Data acquisition; Image quality; Data collection; Data quality; Scanner; Process (computing); Quality (philosophy); Quantitative computed tomography; Repeatability; Medical physics; Reliability engineering; Artificial intelligence; Image (mathematics); Statistics; Mathematics; Bone density; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.005110806,0.001153909,0.001719559,0.003487066,0.0003962387,0.002764312,0.002299187,0.002620403,0.003425926],"category_scores_gemma":[0.01185365,0.0006603952,0.0005988471,0.002447339,0.002261922,0.003681934,0.001025405,0.002967505,0.002070581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007462327,"about_ca_system_score_gemma":0.003458426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003426034,"about_ca_topic_score_gemma":0.004707941,"domain_scores_codex":[0.998484,0.0003337637,0.0002235074,0.0002140457,0.0006872679,0.0000574279],"domain_scores_gemma":[0.9870425,0.009154136,0.0005421407,0.0003976764,0.00267626,0.0001872272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005380798,0.00005051274,0.00186145,0.01542425,0.0000906963,0.000752365,0.0002261494,0.001191506,0.005001318,0.01646598,0.02215013,0.9367319],"study_design_scores_gemma":[0.00002399308,0.000106184,0.004449976,0.009763185,0.0002784267,0.01115611,0.00066079,0.001165236,0.004692804,0.02443281,0.9431654,0.0001051342],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005991229,0.9817121,0.009671899,0.003250711,0.0006492808,0.00002977491,0.000118726,0.0000400493,0.003928449],"genre_scores_gemma":[0.009838164,0.9541917,0.0306449,0.001656097,0.001589924,0.00009304554,0.0002768367,0.00004222408,0.001667154],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005110806,"threshold_uncertainty_score":0.0270288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2283787523455181,"score_gpt":0.426100678791722,"score_spread":0.197721926446204,"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."}}