{"id":"W2484435683","doi":"","title":"Feature-Based Bi-Planar RSA for Kinematic Analysis of Total Knee Arthroplasty","year":2012,"lang":"en","type":"article","venue":"","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of British Columbia","funders":"","keywords":"Kinematics; Fluoroscopy; Computer science; Software; Sagittal plane; Artificial intelligence; Radiography; CAD; Feature (linguistics); Computer vision; Medicine; Engineering drawing; Engineering; Surgery; Radiology; Physics","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":[0.0007480686,0.000847882,0.0005034157,0.001361609,0.0001672542,0.0007685829,0.0006829021,0.0004870749,0.007367056],"category_scores_gemma":[0.003775677,0.0003912171,0.0007822094,0.00133936,0.0002631801,0.0005323358,0.0006462245,0.0004831052,0.002252605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002955223,"about_ca_system_score_gemma":0.0004266213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009204742,"about_ca_topic_score_gemma":0.001131358,"domain_scores_codex":[0.9993149,0.000135395,0.00005268097,0.0001787264,0.000290293,0.00002797236],"domain_scores_gemma":[0.998731,0.0005200897,0.0002424169,0.0001949082,0.0002811603,0.0000304375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002331146,0.00008350286,0.005811566,0.0006875962,0.0001271636,0.0002060434,0.0002114222,0.08557285,0.1075305,0.004899398,0.002498762,0.792138],"study_design_scores_gemma":[0.0000281402,0.0004872669,0.03145286,0.0001657117,0.00009611242,0.001708334,0.0001393496,0.8732014,0.06287213,0.007732922,0.02194879,0.0001669729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01241393,0.0003721932,0.9835671,0.00003190507,0.00002673335,0.00006813742,0.0004624297,0.002006284,0.001051246],"genre_scores_gemma":[0.1758417,0.0004633884,0.8209395,0.00002608028,0.00003193214,0.0002907918,0.0007686566,0.0003892402,0.001248696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007367056,"threshold_uncertainty_score":0.02464521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653240620766799,"score_gpt":0.2709262944688934,"score_spread":0.2543938882612254,"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."}}