{"id":"W4294266718","doi":"10.1038/s41598-022-19282-6","title":"A flexible MRI coil based on a cable conductor and applied to knee imaging","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institutes of Health; School of Medicine, New York University; National Institute of Biomedical Imaging and Bioengineering; York University; Center for Advanced Imaging Innovation and Research","keywords":"Conductor; Electromagnetic coil; Magnetic resonance imaging; Computer science; Biomedical engineering; Medicine; Electrical engineering; Materials science; Radiology; Engineering; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003165697,0.000639746,0.0002502502,0.0004296461,0.0001454996,0.0002389068,0.0002783797,0.0005290648,0.000957361],"category_scores_gemma":[0.0004621035,0.0002228254,0.0002608832,0.0004079326,0.0004493467,0.0003732766,0.0003366584,0.000206438,0.0005190391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001822667,"about_ca_system_score_gemma":0.0003077962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004071764,"about_ca_topic_score_gemma":0.000412901,"domain_scores_codex":[0.9998148,0.0000364051,0.0000123814,0.00006645608,0.00004415461,0.00002588729],"domain_scores_gemma":[0.9997703,0.00003540005,0.00004990817,0.00003528209,0.00006762925,0.0000414367],"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":[0.0001584342,0.00001682582,0.0006529266,0.0003163658,0.00001531862,0.0008839393,0.0001182019,0.002691158,0.9552552,0.001643412,0.001175463,0.03707274],"study_design_scores_gemma":[0.00009087772,0.002568695,0.004916954,0.00008791898,0.000112888,0.004950474,0.0001045822,0.02324358,0.9113194,0.001204642,0.05128099,0.0001189051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2227298,0.003827816,0.7624746,0.0006728419,0.0005028939,0.0004062129,0.0003247668,0.001363154,0.007697937],"genre_scores_gemma":[0.5762394,0.002062666,0.4153021,0.000395093,0.0001672227,0.0002588786,0.000284936,0.0001624726,0.005127268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000957361,"threshold_uncertainty_score":0.003202736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644849274025839,"score_gpt":0.2944002521508611,"score_spread":0.2779517594106027,"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."}}