{"id":"W2897491432","doi":"10.1016/j.jmbbm.2018.10.013","title":"Development of a validated glenoid trabecular density-modulus relationship","year":2018,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre; St Joseph's Health Care; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Lawson Health Research Institute","keywords":"Finite element method; Trabecular bone; Modulus; Tomography; Quantitative computed tomography; Computed tomography; Bone density; Materials science; Resolution (logic); Homogeneous; Mathematics; Geometry; Physics; Medicine; Statistical physics; Computer science; Structural engineering; Radiology; Engineering; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001994059,0.0004733329,0.0004501448,0.001113371,0.0002909137,0.0007263421,0.001204193,0.0006735458,0.003215223],"category_scores_gemma":[0.005642165,0.0003696719,0.0004149839,0.0007799962,0.000213373,0.000554234,0.0007233057,0.0003777839,0.00153725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004788604,"about_ca_system_score_gemma":0.001045676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002925442,"about_ca_topic_score_gemma":0.006724961,"domain_scores_codex":[0.9992341,0.000143734,0.00005911745,0.0001700168,0.0003605087,0.0000326591],"domain_scores_gemma":[0.998364,0.0005619389,0.0002240256,0.0001573897,0.0006589321,0.0000336858],"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":[0.0007312845,0.0009853801,0.1926521,0.0006701674,0.0005190962,0.000652999,0.0004023546,0.1109847,0.2929857,0.005183465,0.005917691,0.3883151],"study_design_scores_gemma":[0.0002945719,0.001411041,0.1692535,0.0001384492,0.000306519,0.001444556,0.0001808864,0.6708724,0.1360998,0.002284614,0.01756905,0.0001446472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3961846,0.0007071624,0.5881755,0.000143219,0.00008205476,0.0007518991,0.004456342,0.002629342,0.006869799],"genre_scores_gemma":[0.8017364,0.0003756519,0.1903961,0.0001115495,0.00002419087,0.0006869498,0.003610899,0.0002729505,0.002785288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003215223,"threshold_uncertainty_score":0.01075602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04162466432929146,"score_gpt":0.3227929666924365,"score_spread":0.2811683023631451,"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."}}