{"id":"W4390406796","doi":"10.1002/rcs.2618","title":"A magnetic resonance conditional robot for lumbar spinal injection: Development and preliminary validation","year":2023,"lang":"en","type":"article","venue":"International Journal of Medical Robotics and Computer Assisted Surgery","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Imaging phantom; Magnetic resonance imaging; Robot; Lumbar; Computer science; Scanner; Rotation (mathematics); Tracking (education); Biomedical engineering; Simulation; Computer vision; Artificial intelligence; Nuclear medicine; Radiology; Medicine","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":[0.001398206,0.000563413,0.0004164728,0.0003843096,0.0002415297,0.0004535395,0.00143339,0.0009675937,0.002752801],"category_scores_gemma":[0.001830177,0.0002031192,0.0003737299,0.0001326998,0.0005816022,0.0005187725,0.0005631308,0.0004240256,0.0008302611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002703271,"about_ca_system_score_gemma":0.001168184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007329492,"about_ca_topic_score_gemma":0.000547388,"domain_scores_codex":[0.9991587,0.0001332775,0.00006002019,0.0001069237,0.0004551971,0.00008574641],"domain_scores_gemma":[0.9988973,0.0001833315,0.0001522672,0.0001402006,0.0005238166,0.0001030442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001514267,0.001504388,0.01178746,0.002796669,0.0001334742,0.001273665,0.0007081393,0.02416201,0.6826473,0.003458929,0.006062662,0.263951],"study_design_scores_gemma":[0.000670077,0.03240665,0.03962796,0.0004156138,0.0004101212,0.007403223,0.0004212378,0.1664275,0.6721397,0.0008369791,0.07885679,0.0003841223],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3989518,0.001466016,0.5872127,0.0005162469,0.000299162,0.002265522,0.0004546913,0.00344182,0.005391987],"genre_scores_gemma":[0.7261642,0.0005756065,0.2653137,0.0002413457,0.00004505704,0.0009041689,0.0006131704,0.0001092855,0.006033538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002752801,"threshold_uncertainty_score":0.009209096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901292794915008,"score_gpt":0.2743790252469864,"score_spread":0.2453660972978363,"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."}}