{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004902739,0.0001027341,0.0001811127,0.000225805,0.00006990975,0.00008517026,0.0001452014,0.00008682277,0.00001279242],"category_scores_gemma":[0.00008089805,0.000100165,0.00006397049,0.0001283229,0.00005026568,0.00008365879,0.00005882821,0.0001504248,0.000002762936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004000018,"about_ca_system_score_gemma":0.0001048762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.778386e-7,"about_ca_topic_score_gemma":8.818602e-7,"domain_scores_codex":[0.9986466,0.00001686708,0.000483472,0.0001146334,0.000607208,0.0001312685],"domain_scores_gemma":[0.9991377,0.0003412799,0.00009070591,0.00004912763,0.0002303,0.0001509156],"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":[0.00007329143,0.0001272617,0.003822186,0.0001101636,0.0002280159,0.0001493564,0.000158864,0.08884807,0.00005958778,0.004659735,0.01715478,0.8846087],"study_design_scores_gemma":[0.0008787531,0.0001911031,0.3680883,0.0005170145,0.00003866427,0.001142841,0.000038444,0.6103943,0.00008988482,0.001595278,0.01676622,0.0002591253],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1385844,0.001773334,0.853106,0.003950423,0.002332141,0.0001329639,0.00001341264,0.00008618783,0.00002120539],"genre_scores_gemma":[0.9641268,0.001035987,0.03325284,0.0002452009,0.001159662,0.00002774819,0.00008880714,0.00002666719,0.00003633726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8843496,"threshold_uncertainty_score":0.408461,"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."}}