{"id":"W4280518917","doi":"10.1016/j.compbiomed.2022.105646","title":"Formulation and exploration of novel, intramuscular pressure based, muscle activation strategies in a spine model","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal General Hospital","funders":"Materials Research Science and Engineering Center, Harvard University; Fonds de recherche du Québec – Nature et technologies; Instituto Mexicano del Petróleo; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Leverage (statistics); Computer science; Limiting; Muscle contraction; Core stability; Physical medicine and rehabilitation; Medicine; Anatomy; Artificial intelligence; Engineering","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":[0.0004488056,0.0008280922,0.0007416189,0.0003795573,0.0003510214,0.0009295572,0.0008022357,0.002290064,0.002225842],"category_scores_gemma":[0.001235192,0.0004539837,0.0004988404,0.0002263878,0.000896941,0.0007381901,0.0008773517,0.0007322636,0.0003197683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004855157,"about_ca_system_score_gemma":0.001044795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002631013,"about_ca_topic_score_gemma":0.002948217,"domain_scores_codex":[0.9998914,0.00003455274,0.000005934357,0.00002212169,0.0000310379,0.00001499923],"domain_scores_gemma":[0.9996712,0.0001886508,0.00004772869,0.00001514251,0.00005342573,0.00002381766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002932409,0.00004787408,0.0001496478,0.0001043178,0.00001388124,0.00008230013,0.00005707493,0.9722534,0.006044779,0.01640953,0.0003184003,0.004489464],"study_design_scores_gemma":[0.000008844857,0.00003081122,0.00004027101,0.000004924369,0.000004745078,0.000008219058,0.00001199198,0.997888,0.0003102984,0.001394942,0.0002934757,0.00000354936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06146494,0.0004249129,0.921099,0.0007980098,0.00006310794,0.0001550266,0.0001390399,0.0001256636,0.0157303],"genre_scores_gemma":[0.86243,0.000724641,0.1192855,0.0002445707,0.00007756648,0.0006400853,0.0001080362,0.0001194184,0.0163702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002631013,"threshold_uncertainty_score":0.00744617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458047729072317,"score_gpt":0.2706571605360783,"score_spread":0.2460766832453551,"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."}}