{"id":"W4412196441","doi":"10.1109/tnsre.2025.3587233","title":"DAGAN-Based Gait Features Augmentation for Ankle Instability Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Sports injuries and prevention","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Gait; Artificial intelligence; Kinematics; Histogram; Pattern recognition (psychology); Visualization; Data set; Gait analysis; Physical medicine and rehabilitation; Medicine","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":[0.0003187936,0.0006258869,0.0003955267,0.0008593852,0.0001623607,0.0001999877,0.0004814216,0.0003064137,0.001320647],"category_scores_gemma":[0.0008308321,0.0002101114,0.0004603002,0.0005065092,0.0001668189,0.0003453436,0.0005120565,0.0004486268,0.0004545276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003505766,"about_ca_system_score_gemma":0.0004791252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004390814,"about_ca_topic_score_gemma":0.008463115,"domain_scores_codex":[0.9998335,0.00002553569,0.000008557211,0.0000527415,0.00004490394,0.00003473477],"domain_scores_gemma":[0.9998239,0.00005241137,0.00002026014,0.00002688911,0.00006185471,0.00001479253],"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":[0.0005021884,0.0003666997,0.00821663,0.0001321499,0.0001289618,0.0002544564,0.00005162034,0.1863343,0.05070299,0.001641023,0.004805927,0.7468631],"study_design_scores_gemma":[0.000007251278,0.00008796791,0.0037476,0.000006493683,0.00002485317,0.0001046807,0.000009000665,0.9825049,0.01158053,0.000792888,0.001123584,0.00001037467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2648303,0.0008105233,0.7246726,0.0003399513,0.0002312739,0.0001278021,0.001215141,0.004079157,0.003693315],"genre_scores_gemma":[0.8720953,0.0002287982,0.1213944,0.0001717523,0.00004112287,0.00007871618,0.001812495,0.00008363997,0.004093734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004390814,"threshold_uncertainty_score":0.008730471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006915126719301514,"score_gpt":0.2581523836493951,"score_spread":0.2512372569300936,"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."}}