{"id":"W2951997750","doi":"10.1126/scirobotics.aav7725","title":"Intelligent magnetic manipulation for gastrointestinal ultrasound","year":2019,"lang":"en","type":"article","venue":"Science Robotics","topic":"Gastrointestinal Bleeding Diagnosis and Treatment","field":"Medicine","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"FujiFilm VisualSonics (Canada)","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Biomedical Imaging and Bioengineering; Engineering and Physical Sciences Research Council; Royal Society","keywords":"Clipping (morphology); Gastrointestinal tract; Medicine; Endoscopy; Radiology; Visualization; Ultrasound; Medical diagnosis; Computer science; Artificial intelligence; Internal 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":[0.0002126822,0.0001824941,0.00008576847,0.0001461956,0.0001014288,0.0002884587,0.0002059157,0.0002891879,0.001291488],"category_scores_gemma":[0.0003652636,0.0001152939,0.0001567134,0.00005670834,0.0003053722,0.0001849573,0.0002182364,0.0002332857,0.000351634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001647151,"about_ca_system_score_gemma":0.000202325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003816341,"about_ca_topic_score_gemma":0.0006738899,"domain_scores_codex":[0.9999065,0.00002411094,0.000004892579,0.00001929296,0.00003530288,0.000009912298],"domain_scores_gemma":[0.9999032,0.00003066072,0.00002969824,0.00001440909,0.000009052278,0.0000130377],"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":[0.0001573429,0.00004282822,0.0003080226,0.00007925489,0.000007793695,0.0001053567,0.00005183179,0.001374484,0.9617624,0.0009725597,0.0002426771,0.03489557],"study_design_scores_gemma":[0.0001166873,0.003219743,0.009872651,0.00008792459,0.00007730814,0.002330223,0.00006429327,0.03810667,0.8931535,0.001155165,0.0517644,0.00005137672],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5075062,0.00305327,0.4717868,0.0005377996,0.0002556801,0.0002837664,0.0001384853,0.00140354,0.01503441],"genre_scores_gemma":[0.8952241,0.0006946708,0.09601824,0.0001871706,0.00003356136,0.0001689021,0.0001070735,0.00004172045,0.007524575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001291488,"threshold_uncertainty_score":0.004320443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099252222833527,"score_gpt":0.2946574170796194,"score_spread":0.2636648948512841,"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."}}