{"id":"W7018318225","doi":"","title":"Development and validation of a timely and representative fnite element human spine model for biomechanical simulations","year":2020,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Spine and Intervertebral Disc Pathology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Torso; Lumbar spine; Finite element method; Low back pain; Biomechanics; Set (abstract data type); Displacement (psychology)","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":[0.0006481798,0.0005880363,0.0004974135,0.0004478054,0.0004736994,0.0008610726,0.001812521,0.001368103,0.006990784],"category_scores_gemma":[0.001893876,0.0004924402,0.0005220757,0.0003129952,0.0003850075,0.0005861234,0.0007541078,0.0006939364,0.002270642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005671084,"about_ca_system_score_gemma":0.001808137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01062952,"about_ca_topic_score_gemma":0.01412377,"domain_scores_codex":[0.9996656,0.00005787761,0.00002180131,0.00004165179,0.0001912083,0.00002182513],"domain_scores_gemma":[0.9993294,0.0001546161,0.00005145548,0.0001169279,0.0003179732,0.00002964861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006586544,0.0000883785,0.001339188,0.0001138361,0.00001712033,0.000163059,0.0001899905,0.9418878,0.01394987,0.002774627,0.002866923,0.03654331],"study_design_scores_gemma":[0.00001707197,0.00005565932,0.0005500502,0.00002045408,0.000008175512,0.0000734016,0.00005196055,0.9846193,0.004693117,0.0006315921,0.009265922,0.00001339702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04516397,0.00009439314,0.9359939,0.0003033835,0.0001536605,0.0004195701,0.001348975,0.001817947,0.01470423],"genre_scores_gemma":[0.5948223,0.0002538432,0.3773843,0.0001667374,0.00003351237,0.001015849,0.002526213,0.0007027704,0.02309435],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01062952,"threshold_uncertainty_score":0.02338654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08431080658093523,"score_gpt":0.3324701781702304,"score_spread":0.2481593715892952,"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."}}