{"id":"W2922369615","doi":"10.1080/10255842.2019.1584795","title":"Customized k-nearest neighbourhood analysis in the management of adolescent idiopathic scoliosis using 3D markerless asymmetry analysis","year":2019,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Sick Kids Foundation; Stollery Children’s Hospital Foundation; Scoliosis Research Society","keywords":"Torso; Scoliosis; Medicine; Idiopathic scoliosis; Radiography; Curvature; Deformity; Lumbar; Nuclear medicine; Radiology; Artificial intelligence; Orthodontics; Mathematics; Surgery; Anatomy; Computer science; Geometry","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":[0.001005035,0.0004534984,0.0009492185,0.00149782,0.0003564865,0.0007381355,0.0009284256,0.0008554356,0.000851848],"category_scores_gemma":[0.003180744,0.0002709301,0.0008231797,0.0006466315,0.000266564,0.0005706422,0.0007774882,0.0004788595,0.0005345733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004250375,"about_ca_system_score_gemma":0.0006418268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005182455,"about_ca_topic_score_gemma":0.007331522,"domain_scores_codex":[0.9990613,0.0001823651,0.00009464857,0.0001990365,0.0003868778,0.00007581937],"domain_scores_gemma":[0.9992757,0.0002667989,0.0001047332,0.00005428363,0.0002413668,0.00005707786],"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":[0.0007287788,0.000298771,0.04930379,0.0003217039,0.0002055117,0.0007270607,0.000340954,0.1238897,0.02630405,0.001340099,0.002782776,0.7937568],"study_design_scores_gemma":[0.00004492084,0.0002629051,0.01660001,0.00007073332,0.00008772456,0.001131023,0.0001164699,0.9687038,0.008844604,0.001641904,0.002433169,0.00006280385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3089622,0.002155202,0.682784,0.0003305718,0.0001471075,0.0003769366,0.0003536919,0.001668844,0.003221528],"genre_scores_gemma":[0.8194618,0.0005199988,0.1783315,0.00007869405,0.0000507114,0.0001299107,0.0003417329,0.0001004769,0.0009851923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005182455,"threshold_uncertainty_score":0.01030457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379354044026396,"score_gpt":0.3433823737669598,"score_spread":0.3195888333266959,"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."}}