{"id":"W3100004966","doi":"10.1109/ius46767.2020.9251309","title":"An Ultrasonically Actuated Fine Needle Enhances Biopsy Sample Yield","year":2020,"lang":"en","type":"article","venue":"","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"Academy of Finland","keywords":"Biopsy; Fine-needle aspiration; Yield (engineering); Tissue sample; Ultrasound; Biomedical engineering; Ex vivo; Materials science; Ultrasonic sensor; Medicine; Radiology; In vivo; Composite material; Biology","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":[0.0003167816,0.0002113297,0.0001772232,0.0001951294,0.00008691223,0.0002219977,0.0001767994,0.0003911747,0.0006517957],"category_scores_gemma":[0.0006155745,0.0001356483,0.0001312624,0.0001190731,0.0002164743,0.0002302585,0.00027383,0.0001809901,0.0001932242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140201,"about_ca_system_score_gemma":0.00008177018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001787729,"about_ca_topic_score_gemma":0.0004672494,"domain_scores_codex":[0.9998061,0.00003188326,0.00001196683,0.0000409024,0.00008188866,0.00002736807],"domain_scores_gemma":[0.9996871,0.0001498877,0.00006424372,0.00002345716,0.00004561927,0.00002972925],"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":[0.00002532704,0.000003799561,0.000166144,0.00001731321,0.00000105542,0.00002265713,0.00000963914,0.00003123823,0.9978681,0.00001431305,0.0000102913,0.001830094],"study_design_scores_gemma":[0.000004835688,0.0003558158,0.008585329,0.000005653072,0.00001408509,0.0004330421,0.00002814469,0.001696971,0.9870287,0.00003227205,0.001805959,0.000009160234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9550074,0.002092221,0.04133028,0.00008759159,0.00004826581,0.00004286305,0.00006634196,0.0001828332,0.001142247],"genre_scores_gemma":[0.9623613,0.0007655943,0.0354748,0.00006709579,0.00001985598,0.0000299418,0.00008748636,0.0000283659,0.001165454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006517957,"threshold_uncertainty_score":0.002180517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02086813336993405,"score_gpt":0.2266892800858813,"score_spread":0.2058211467159472,"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."}}