{"id":"W4414809704","doi":"10.1177/09544119251361341","title":"Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine","topic":"Sports injuries and prevention","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Natural Science Foundation of Ningbo","keywords":"Sagittal plane; Ankle; Knee Joint; Joint (building); Moment (physics); Biomechanics; Cricket","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":[0.0004128603,0.0007883178,0.0003443652,0.0003672561,0.0001415931,0.0003423282,0.0005704644,0.0005226498,0.0008548066],"category_scores_gemma":[0.001292386,0.0002843788,0.0004023672,0.0001830694,0.000188834,0.000364715,0.0003656922,0.0004791577,0.0002044903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003365627,"about_ca_system_score_gemma":0.0004398308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01150788,"about_ca_topic_score_gemma":0.01112499,"domain_scores_codex":[0.9998755,0.00003098548,0.000007030324,0.00003776757,0.00002358982,0.0000250452],"domain_scores_gemma":[0.9997066,0.0001299967,0.00004917333,0.00001823457,0.000075745,0.0000201779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002195767,0.0001125272,0.01103804,0.00005236311,0.00007976514,0.0001721178,0.00007861886,0.9139226,0.01021935,0.0003741229,0.0004765495,0.0632543],"study_design_scores_gemma":[0.000001564284,0.00002174486,0.001334694,0.000001493262,0.000004353064,0.000007902483,0.000004600984,0.998075,0.0004380246,0.00008742452,0.00001987539,0.000003262903],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5768956,0.0002733629,0.4195802,0.0001723386,0.00007154789,0.00007342637,0.0002268823,0.001098444,0.001608302],"genre_scores_gemma":[0.9837141,0.00005639407,0.01525055,0.00001627335,0.000008455123,0.00002737464,0.0001139414,0.00001980859,0.0007931638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01150788,"threshold_uncertainty_score":0.02288175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336540257173095,"score_gpt":0.2381367795767318,"score_spread":0.2247713770050009,"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."}}