{"id":"W4391452153","doi":"10.1016/j.jbiomech.2024.111967","title":"Machine learning applications in spine biomechanics","year":2024,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia; Institut de recherche Robert-Sauvé en santé et en sécurité du travail; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biomechanics; Kinematics; Computer science; Sports biomechanics; Flexibility (engineering); Artificial intelligence; Simulation; Human–computer interaction; Medicine","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":[0.002421007,0.0009232637,0.0007859845,0.001758248,0.0004144467,0.001731378,0.0009730625,0.001534736,0.003911803],"category_scores_gemma":[0.008287654,0.000312354,0.0007056513,0.002433047,0.0006082171,0.001118841,0.001140084,0.001516327,0.001386245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006100453,"about_ca_system_score_gemma":0.0009821366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002372913,"about_ca_topic_score_gemma":0.002155475,"domain_scores_codex":[0.9987005,0.0005414014,0.0001000267,0.000205859,0.0003868019,0.00006540005],"domain_scores_gemma":[0.9950288,0.003477026,0.0002161482,0.0003556724,0.0008289339,0.00009346822],"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":[0.000100971,0.0002428481,0.009526093,0.0006375408,0.00017228,0.0002902014,0.0001812503,0.1642612,0.00544442,0.02515235,0.009705978,0.7842848],"study_design_scores_gemma":[0.00001487913,0.00009938582,0.003836433,0.0001903989,0.00003337298,0.0002493208,0.0001237403,0.9264221,0.003066952,0.04845764,0.01746827,0.00003754337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02373462,0.01654885,0.9381254,0.004611772,0.0006685003,0.0001636173,0.0004399872,0.001723124,0.01398425],"genre_scores_gemma":[0.5260558,0.01481705,0.4481468,0.001079363,0.001071993,0.0003222651,0.0007513402,0.0001915337,0.007563849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003911803,"threshold_uncertainty_score":0.01308626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032293951379415,"score_gpt":0.2917059961412596,"score_spread":0.2813830566274654,"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."}}