{"id":"W4413381273","doi":"10.1002/aisy.202500478","title":"Robotic Needle Steering for Percutaneous Interventions: Sensing, Modeling, and Control","year":2025,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Percutaneous; Psychological intervention; Control (management); Computer science; Medicine; Physical medicine and rehabilitation; Artificial intelligence; Surgery; Nursing","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":[0.0006364104,0.000771863,0.0008165502,0.0005404163,0.000180626,0.001029173,0.0008612912,0.001092855,0.0009785551],"category_scores_gemma":[0.0008622162,0.0004657446,0.0008474789,0.0004868303,0.0005330861,0.001118797,0.000510302,0.0007135859,0.000508896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004434999,"about_ca_system_score_gemma":0.0006595907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001263236,"about_ca_topic_score_gemma":0.001051806,"domain_scores_codex":[0.999606,0.00006363364,0.0000341146,0.00006985377,0.0002014347,0.0000250819],"domain_scores_gemma":[0.9996768,0.0001501464,0.00006310656,0.00002234596,0.0000756912,0.00001183186],"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":[0.0001423491,0.0001085114,0.001472376,0.005652507,0.0001587609,0.0003883895,0.0002719185,0.3475781,0.1277278,0.04600962,0.004059618,0.46643],"study_design_scores_gemma":[0.00002223592,0.0004853735,0.001673745,0.000569291,0.0001261857,0.0007846089,0.00009334667,0.8608477,0.03854439,0.01791817,0.07878347,0.0001515345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01167301,0.06417167,0.9143338,0.0005157599,0.000217774,0.0001003035,0.0001125031,0.0007236448,0.008151577],"genre_scores_gemma":[0.514708,0.1876044,0.2873752,0.0004275466,0.0004533375,0.0005883014,0.0004633988,0.0001653532,0.008214461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001263236,"threshold_uncertainty_score":0.003365695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01970877007187473,"score_gpt":0.2730539712434555,"score_spread":0.2533452011715808,"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."}}