{"id":"W4414163830","doi":"10.1016/j.clinbiomech.2025.106663","title":"Wearable monitoring for rehabilitation: Deep learning-driven vertical ground reaction force estimation for anterior cruciate ligament reconstruction","year":2025,"lang":"en","type":"article","venue":"Clinical Biomechanics","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Zhejiang Province; National Key Research and Development Program of China","keywords":"Wearable computer; Anterior cruciate ligament reconstruction; Key (lock); Wearable technology; Rehabilitation; Anterior cruciate ligament","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103706,0.0001597574,0.0005601799,0.0002453392,0.0002613968,0.00005015169,0.00006845719,0.0003698101,0.00000777666],"category_scores_gemma":[0.002158609,0.0001511677,0.0004469143,0.0002408792,0.0001153238,0.0001743037,0.00003351517,0.000260463,0.000005283968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329682,"about_ca_system_score_gemma":0.00009479571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006222118,"about_ca_topic_score_gemma":0.000001408764,"domain_scores_codex":[0.9979036,0.00005983118,0.001229538,0.0004347493,0.0001267383,0.0002454868],"domain_scores_gemma":[0.99826,0.0007809255,0.0002458223,0.0002294452,0.0003777435,0.0001060692],"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":[0.00107986,0.0001494093,0.001135539,0.0004837155,0.0001131721,3.856228e-7,0.00004135148,0.00001015573,0.02578626,0.0006223157,0.00005907911,0.9705188],"study_design_scores_gemma":[0.01295582,0.01928306,0.006846301,0.01667835,0.001499756,0.0001621042,0.002987725,0.6180896,0.1905271,0.1138888,0.01602856,0.001052781],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4808153,0.0006348722,0.5097775,0.00213496,0.004247138,0.001955451,0.000005445809,0.0003358627,0.00009350628],"genre_scores_gemma":[0.7752848,0.002566002,0.2196911,0.0001510769,0.0006547709,0.0006389866,0.00001324374,0.00003905455,0.0009608926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.969466,"threshold_uncertainty_score":0.6164441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02483658646063486,"score_gpt":0.3765026142412565,"score_spread":0.3516660277806217,"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."}}