{"id":"W2755356387","doi":"10.1016/j.jbiomech.2017.09.014","title":"Analysis of pelvic strain in different gait configurations in a validated cohort of computed tomography based finite element models","year":2017,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Pelvic and Acetabular Injuries","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Hospital for Sick Children; University of Toronto","keywords":"Heel; Pelvis; Gait; Cadaveric spasm; Morphing; Finite element method; Strain (injury); Cohort; Medicine; Materials science; Structural engineering; Geology; Biomedical engineering; Computer science; Anatomy; Artificial intelligence; Engineering; Physical medicine and rehabilitation; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006177819,0.0001358337,0.0008865434,0.001768119,0.00003397784,0.0000194772,0.0002136204,0.00008927425,0.00007693106],"category_scores_gemma":[0.0001011522,0.0001061607,0.0003360131,0.0007098857,0.00005959972,0.0001073699,0.00003089331,0.0001887321,1.838189e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005172167,"about_ca_system_score_gemma":0.0001383729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006380695,"about_ca_topic_score_gemma":0.0001045457,"domain_scores_codex":[0.9981896,0.00007178648,0.001016883,0.0001214615,0.0004406691,0.0001596494],"domain_scores_gemma":[0.9980733,0.00008760992,0.001079725,0.0003509654,0.0003201541,0.00008818771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002044334,0.005791603,0.1806944,0.0006245595,0.009052785,0.0003313539,0.001737575,0.02028168,0.7637537,0.002390578,0.0001722526,0.0131252],"study_design_scores_gemma":[0.003672368,0.0006004592,0.1162417,0.0004714307,0.001629323,0.000004210278,0.0002340207,0.8317836,0.04445987,0.0007450456,0.00002747642,0.0001304869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9419488,0.0001540157,0.05687919,0.0004710264,0.00007013192,0.0002221845,0.0001877288,0.000003659957,0.000063296],"genre_scores_gemma":[0.9986294,0.00006298376,0.001145993,0.0000560592,0.00001734868,0.000002322984,0.00007024074,0.000007823798,0.000007808389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8115019,"threshold_uncertainty_score":0.4329106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02752158654960647,"score_gpt":0.2933327571270269,"score_spread":0.2658111705774204,"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."}}