{"id":"W4412463372","doi":"10.1055/s-0045-1810260","title":"Novel, Markerless, Kinematic Tool Utilising a Deep Neural Network for Analysis of Joint Range of Motion and Lameness In Dogs","year":2025,"lang":"en","type":"article","venue":"Veterinary and Comparative Orthopaedics and Traumatology","topic":"Veterinary Orthopedics and Neurology","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Lameness; Kinematics; Range of motion; Joint (building); Range (aeronautics); Motion analysis; Physical medicine and rehabilitation; Motion (physics); Artificial intelligence; Physical therapy; Surgery; Computer science; Engineering; Structural engineering","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":[0.0004479922,0.0005636753,0.0004530727,0.0009787257,0.0001418513,0.0004474223,0.0006688184,0.0006745135,0.001321363],"category_scores_gemma":[0.0007276505,0.00025066,0.0002890164,0.0005078604,0.0001942133,0.0003847899,0.0005615732,0.0003598215,0.0003756226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002228294,"about_ca_system_score_gemma":0.0004049288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003490539,"about_ca_topic_score_gemma":0.007834277,"domain_scores_codex":[0.9997911,0.00003360609,0.00001255272,0.00007199164,0.00006086518,0.00002991762],"domain_scores_gemma":[0.9997352,0.00007989272,0.00005806613,0.00001900303,0.00007811566,0.00002977878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001371219,0.0003336586,0.02907413,0.0003738021,0.0003013838,0.0005079717,0.0001986813,0.07331663,0.151652,0.001170534,0.006135668,0.7355644],"study_design_scores_gemma":[0.00003832842,0.000349644,0.03147566,0.00004453316,0.00007880555,0.0005011121,0.000100398,0.9436722,0.01982962,0.001403389,0.002460611,0.00004564253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2573799,0.001121428,0.7342163,0.0002506211,0.0001715751,0.0001122114,0.001518227,0.003251158,0.001978666],"genre_scores_gemma":[0.8246737,0.0003920808,0.1688511,0.0001325127,0.00005725359,0.0001241498,0.001226258,0.0001087949,0.004434184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003490539,"threshold_uncertainty_score":0.006940424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1339743121764988,"score_gpt":0.3509288183636399,"score_spread":0.216954506187141,"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."}}