{"id":"W2993517307","doi":"10.1016/j.jbiomech.2019.109550","title":"Subject-specific regression equations to estimate lower spinal loads during symmetric and asymmetric static lifting","year":2019,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Trunk; Electromyography; Kinematics; Regression analysis; Mathematics; Physical medicine and rehabilitation; Low back pain; Physical therapy; Orthodontics; Medicine; Statistics; Physics","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":[0.002350871,0.0007908982,0.000816146,0.0006750684,0.0002102061,0.0004252917,0.0004823481,0.0005278277,0.003278084],"category_scores_gemma":[0.007390691,0.0004708217,0.001166256,0.000632368,0.00008797806,0.0002961397,0.0003746554,0.0007352635,0.000931964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002438312,"about_ca_system_score_gemma":0.0007331982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01440542,"about_ca_topic_score_gemma":0.01801623,"domain_scores_codex":[0.9995345,0.0002139447,0.00004335818,0.0001100267,0.00006053792,0.00003768339],"domain_scores_gemma":[0.9974234,0.001925904,0.0001526467,0.0001050539,0.0003613243,0.00003161814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003573746,0.001359427,0.3652115,0.00066185,0.004392368,0.0002722469,0.0008109719,0.192492,0.02627206,0.001778575,0.006350334,0.396825],"study_design_scores_gemma":[0.0002298955,0.0009721062,0.340096,0.00007550152,0.0009683135,0.0002302798,0.000207062,0.6509534,0.003661114,0.0007708216,0.001766437,0.00006913361],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7183662,0.0005697778,0.2744044,0.0001563745,0.000124209,0.0003486202,0.003454622,0.001176067,0.001399717],"genre_scores_gemma":[0.929311,0.0003749218,0.05931531,0.00005301915,0.0000320475,0.0005993636,0.004743026,0.0002132694,0.005358147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01440542,"threshold_uncertainty_score":0.02864313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399196844691112,"score_gpt":0.3092852446838002,"score_spread":0.2952932762368891,"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."}}