{"id":"W4388522437","doi":"10.1186/s12984-023-01273-x","title":"A pelvic kinematic approach for calculating hip angles for active hip disarticulation prosthesis control","year":2023,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Mitacs","keywords":"Pelvis; Kinematics; Prosthesis; Computer science; Ankle; Motion capture; Physical medicine and rehabilitation; Pelvic tilt; Gait; Orthodontics; Simulation; Motion (physics); Algorithm; Medicine; Computer vision; Artificial intelligence; Surgery; Physics","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":[0.0003924076,0.0001411311,0.0002642793,0.0002632507,0.00007755719,0.00003974143,0.00005293447,0.00005733912,3.483452e-7],"category_scores_gemma":[0.0009994727,0.0001193959,0.0001631633,0.0002241323,0.00003383441,0.0001647288,0.000005687156,0.00009884407,3.153671e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004714599,"about_ca_system_score_gemma":0.00001174798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.266312e-7,"about_ca_topic_score_gemma":9.455886e-8,"domain_scores_codex":[0.9990274,0.00002944114,0.0004785425,0.0001194812,0.0001488511,0.000196297],"domain_scores_gemma":[0.9980158,0.001493549,0.0001216894,0.00008668698,0.0002206217,0.00006170122],"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":[0.00005531166,0.00002881372,0.0002538569,0.001346289,0.00004670161,4.900397e-7,0.001454094,0.9709231,0.02067545,0.0002782344,0.00008337102,0.004854328],"study_design_scores_gemma":[0.001156144,0.0005282366,0.02186237,0.0001033756,0.00007489678,0.00001142906,0.0004962257,0.9743497,0.0002679016,0.0009206982,0.00009738497,0.0001316228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7009621,0.0001022587,0.2971921,0.0005267778,0.0002516255,0.0008332354,0.00001497147,0.000110684,0.000006206819],"genre_scores_gemma":[0.9483095,0.00001324388,0.05141608,0.000008938411,0.00008773937,0.0001041975,0.000008978418,0.00004226485,0.000008983967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2473475,"threshold_uncertainty_score":0.4868823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006222475982376,"score_gpt":0.2284113070836675,"score_spread":0.2183490823238437,"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."}}