{"id":"W2978238580","doi":"10.1115/1.4045048","title":"Multiobjective Design Optimization of a Biconcave Mobile-Bearing Lumbar Total Artificial Disk Considering Spinal Kinematics, Facet Joint Loading, and Metal-on-Polyethylene Contact Mechanics","year":2019,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Spine and Intervertebral Disc Pathology","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multi-objective optimization; Lumbar; Computer science; Metric (unit); Facet (psychology); Kinematics; Facet joint; California bearing ratio; Biomedical engineering; Materials science; Surgery; Engineering; Composite material; Medicine; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006964185,0.0007991684,0.0005797515,0.0005656379,0.0002045959,0.0006733795,0.0005900231,0.0008971948,0.0007908417],"category_scores_gemma":[0.0007985571,0.0003427142,0.00053114,0.0002922088,0.0003456753,0.0003682186,0.0004869593,0.0003163781,0.0001271425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004285453,"about_ca_system_score_gemma":0.000640591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001958789,"about_ca_topic_score_gemma":0.001741489,"domain_scores_codex":[0.9996766,0.00009276513,0.00002033654,0.00006622024,0.0001096146,0.00003439913],"domain_scores_gemma":[0.9996823,0.0001411106,0.00006019637,0.00001365267,0.00008256637,0.00002021243],"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":[0.00005192972,0.0000421821,0.000511313,0.0001085219,0.00002032011,0.00006882023,0.00002332162,0.9774169,0.007554455,0.0005760295,0.00008651524,0.0135397],"study_design_scores_gemma":[0.000006830495,0.000117338,0.0002520563,0.000006825911,0.00001195006,0.00001496511,0.0000113406,0.9981118,0.0009205209,0.0002290447,0.0003125216,0.000004674458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3532122,0.001595602,0.632251,0.0001723789,0.00006698968,0.0001607079,0.00009271771,0.0001946656,0.01225374],"genre_scores_gemma":[0.9162287,0.0003470353,0.08021838,0.00005683174,0.000009595991,0.0002353237,0.00007936749,0.00002247843,0.002802273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001958789,"threshold_uncertainty_score":0.003894746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02928459955321933,"score_gpt":0.2687067848982855,"score_spread":0.2394221853450662,"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."}}