{"id":"W2890865904","doi":"10.1016/j.bone.2018.09.014","title":"Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort","year":2018,"lang":"en","type":"article","venue":"Bone","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Alberta Bone and Joint Health Institute","funders":"Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Medicine; Retrospective cohort study; Receiver operating characteristic; Orthodontics; Fracture (geology); Hip fracture; Finite element method; Logistic regression; Femur; Femur fracture; Anthropometry; Surgery; Structural engineering; Osteoporosis; Geology; Internal medicine; Geotechnical engineering; Engineering","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":[0.002441438,0.0001721706,0.0006835926,0.0001046953,0.0001360511,0.00002324328,0.00017105,0.0001937684,0.0001516483],"category_scores_gemma":[0.001342519,0.0001137622,0.00008322463,0.0003514373,0.0001543493,0.00009019567,0.00008658665,0.0006011003,0.00001378709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002257625,"about_ca_system_score_gemma":0.0003873899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001731358,"about_ca_topic_score_gemma":0.001428438,"domain_scores_codex":[0.9972807,0.0002427174,0.0008854545,0.0004864891,0.0005600827,0.0005445693],"domain_scores_gemma":[0.9974736,0.0009790191,0.0002598288,0.0005960986,0.0004277296,0.0002637459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004739201,0.001964072,0.9544956,0.00009248017,0.000249062,0.00003012453,0.008837747,0.00001712467,0.0004704648,0.0000340164,0.009649365,0.01942074],"study_design_scores_gemma":[0.007412162,0.00819234,0.9720026,0.0001447371,0.00006341172,0.000001605025,0.005223412,0.003003059,0.0001637414,0.001093521,0.002565761,0.0001337117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925351,0.0004063886,0.0002715259,0.003225711,0.0001137298,0.003187313,0.00006611304,0.00002317991,0.0001709273],"genre_scores_gemma":[0.9963259,0.0002359842,0.0004454823,0.001803713,0.0004235179,0.0004808153,0.00003322206,0.00002885915,0.0002225758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01928703,"threshold_uncertainty_score":0.4639087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1524798024678015,"score_gpt":0.439773451101628,"score_spread":0.2872936486338265,"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."}}