{"id":"W2913493483","doi":"10.1016/j.bone.2019.01.024","title":"Assessing bone impairment in ankylosing spondylitis (AS) using the trabecular bone score (TBS) and high-resolution peripheral quantitative computed tomography (HR-pQCT)","year":2019,"lang":"en","type":"article","venue":"Bone","topic":"Spondyloarthritis Studies and Treatments","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Nacional de Ciência e Tecnologia Midas","keywords":"Quantitative computed tomography; Trabecular bone score; Medicine; Bone mineral; Tibia; Ankylosing spondylitis; Nuclear medicine; Bone density; Cortical bone; Osteoporosis; Internal medicine; Surgery; Anatomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003450979,0.0003071052,0.0006432675,0.0002390134,0.0002983236,0.0001201326,0.00004731041,0.0001135363,0.0001162247],"category_scores_gemma":[0.00003351865,0.0002415082,0.0001467768,0.0005420098,0.0001947112,0.0002849215,0.0000928884,0.0002653495,0.00001026915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002448009,"about_ca_system_score_gemma":0.00007661173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001688629,"about_ca_topic_score_gemma":0.0002006608,"domain_scores_codex":[0.9980128,0.0001419694,0.0004451791,0.0004908284,0.0003972618,0.0005120079],"domain_scores_gemma":[0.9992195,0.00008997787,0.000165805,0.0003017083,0.0001026774,0.0001203583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004986668,0.003656687,0.7924531,0.0009084464,0.00161664,0.007557763,0.01245368,0.0007547866,0.1357361,0.009484418,0.0003359058,0.03005583],"study_design_scores_gemma":[0.01606772,0.0021461,0.9570733,0.002421777,0.0003757317,0.001473271,0.006265169,0.01115726,0.001088886,0.0004949844,0.000859627,0.000576205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832308,0.01409193,0.000451137,0.0006674302,0.0002353038,0.001044193,0.00004200846,0.00005169676,0.0001855326],"genre_scores_gemma":[0.9924483,0.0001867524,0.006810294,0.0002558975,0.00007526534,0.00002608382,0.00009545134,0.00003950188,0.00006248522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1646202,"threshold_uncertainty_score":0.9848419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488019466066147,"score_gpt":0.2948000648233763,"score_spread":0.2699198701627148,"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."}}