{"id":"W4391929901","doi":"10.1109/oncon60463.2023.10430851","title":"An Integrated Lifting Predictive Model for Lumbar Injury Risk Assessment","year":2023,"lang":"en","type":"article","venue":"","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Computer science; Risk assessment; Lumbar; Risk model; Physical medicine and rehabilitation; Risk analysis (engineering); Medicine; Surgery; Computer security","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":[0.0004508462,0.0007460794,0.0007216563,0.0007052839,0.0003899459,0.0007154093,0.001079403,0.0009043546,0.00233751],"category_scores_gemma":[0.00092693,0.0004972121,0.001003459,0.0004234363,0.0002809697,0.0004619637,0.0007501324,0.0007338158,0.0004140723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004066194,"about_ca_system_score_gemma":0.0009909656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02788005,"about_ca_topic_score_gemma":0.01579153,"domain_scores_codex":[0.9997684,0.0000364181,0.00001719239,0.00006159987,0.00007482964,0.00004152982],"domain_scores_gemma":[0.9997543,0.00008490031,0.00003960805,0.00001144557,0.00009456602,0.00001536103],"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":[0.00002737798,0.00002850957,0.001104125,0.00002969786,0.00002622705,0.0000463543,0.00001926557,0.986333,0.0007769643,0.0005075383,0.0002209332,0.01088011],"study_design_scores_gemma":[0.000002724374,0.00001510095,0.0003152876,0.000004378604,0.00000993248,0.000006535297,0.000002987871,0.9992267,0.00009386255,0.0001974032,0.0001214521,0.000003617559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09130833,0.0007109815,0.8994161,0.0003168034,0.0001140543,0.0001068831,0.0003852184,0.0007771413,0.006864425],"genre_scores_gemma":[0.9705988,0.0004770602,0.02315682,0.00007936533,0.00004804939,0.0002118883,0.0003977861,0.00004788846,0.004982327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02788005,"threshold_uncertainty_score":0.05543554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727384620732869,"score_gpt":0.3567529436458835,"score_spread":0.3394790974385548,"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."}}