{"id":"W3098341950","doi":"10.1016/j.bone.2020.115759","title":"Evaluation of patient tissue selection methods for deriving equivalent density calibration for femoral bone quantitative CT analyses","year":2020,"lang":"en","type":"article","venue":"Bone","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council; Horizon 2020 Framework Programme; European Commission; Whitaker International Program","keywords":"Calibration; Bone density; Osteoporosis; Quantitative computed tomography; Imaging phantom; Femur; Medicine; Bone mineral; Radiology; Finite element method; Retrospective cohort study; Nuclear medicine; Surgery; Mathematics; Statistics; Internal medicine; Physics","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":[0.01237679,0.00083413,0.0004623075,0.0008412428,0.000269169,0.001024502,0.001048942,0.0008528064,0.001887735],"category_scores_gemma":[0.0325379,0.0005370263,0.0004480633,0.0008223468,0.0003306204,0.0004357278,0.0009405791,0.0005971809,0.0006880429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004258011,"about_ca_system_score_gemma":0.0006338789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007576759,"about_ca_topic_score_gemma":0.001245165,"domain_scores_codex":[0.9948196,0.00290946,0.0003192622,0.0005046588,0.001365055,0.00008191016],"domain_scores_gemma":[0.9846239,0.009459741,0.001359527,0.001936219,0.002500776,0.0001197282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003299165,0.000485759,0.08921053,0.0009141511,0.0006153467,0.0004967807,0.001040805,0.118123,0.3389956,0.003622485,0.002173569,0.4410228],"study_design_scores_gemma":[0.0001905943,0.001382137,0.05814233,0.0001488167,0.0004375618,0.003343007,0.0003383812,0.558265,0.366266,0.001877802,0.00939769,0.0002107125],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1373055,0.0007085769,0.8587245,0.000156524,0.00004739889,0.0002967549,0.0003296684,0.001571801,0.0008593957],"genre_scores_gemma":[0.3609083,0.0002929299,0.6366178,0.0001545024,0.00001850936,0.0003661119,0.0004773574,0.000578609,0.0005858975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01237679,"threshold_uncertainty_score":0.06545556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3409327534175178,"score_gpt":0.552217321443252,"score_spread":0.2112845680257341,"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."}}