{"id":"W2153095844","doi":"10.1016/j.jocd.2006.07.007","title":"Analyzing Cortical Bone Cross-Sectional Geometry by Peripheral QCT: Comparison With Bone Histomorphometry","year":2007,"lang":"en","type":"article","venue":"Journal of Clinical Densitometry","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Quantitative computed tomography; Cortical bone; Tibia; Medicine; Cadaver; Densitometry; Peripheral; Geometry; Bone mineral; Biomedical engineering; Nuclear medicine; Anatomy; Mathematics; Osteoporosis; Pathology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01076082,0.0003299476,0.002042821,0.00133886,0.0002772163,0.0001167842,0.0002281937,0.000652982,0.0005586687],"category_scores_gemma":[0.004751191,0.0002469957,0.0007175501,0.002238718,0.0007463875,0.0002712072,0.00009525358,0.0041778,0.00005848799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004382204,"about_ca_system_score_gemma":0.0007990763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003476825,"about_ca_topic_score_gemma":0.00001437541,"domain_scores_codex":[0.9912605,0.0002595222,0.00449098,0.0004946115,0.002498368,0.0009959842],"domain_scores_gemma":[0.9912331,0.002682843,0.001545959,0.0004338504,0.001839604,0.002264658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01094541,0.001610435,0.9604449,0.000152762,0.000269938,0.001605752,0.00001934692,0.000004501055,0.003719547,0.00001022718,0.01355138,0.007665763],"study_design_scores_gemma":[0.007124966,0.004262263,0.9718823,0.0001493186,0.000160323,0.004406647,0.0001442848,0.0001813403,0.001053837,0.00000792173,0.01037509,0.0002516758],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859329,0.004951606,0.006241204,0.0007840777,0.001326698,0.0002241258,0.00001210097,0.00003798426,0.0004893325],"genre_scores_gemma":[0.988937,0.0002758466,0.006169085,0.001409425,0.00217859,8.614946e-7,0.00002084134,0.0000588786,0.0009494719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01143739,"threshold_uncertainty_score":0.9999982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07071493276450008,"score_gpt":0.464048198562534,"score_spread":0.3933332657980339,"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."}}