{"id":"W4405285388","doi":"10.1080/17435889.2024.2439776","title":"Nanomedicine and clinical diagnostics part I: applications in conventional imaging (MRI, X-ray/CT, and ultrasound)","year":2024,"lang":"en","type":"review","venue":"Nanomedicine","topic":"Nanoplatforms for cancer theranostics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medical physics; Medical imaging; Nanomedicine; Modalities; Modality (human–computer interaction); Clinical Practice; Clinical imaging; Magnetic resonance imaging; Computer science; Nanotechnology; Medicine; Artificial intelligence; Radiology; Materials science; Nanoparticle","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000666217,0.000613065,0.001841425,0.0005004436,0.00005784137,0.00004912852,0.0002213112,0.0001687032,0.00007652734],"category_scores_gemma":[0.0002088694,0.0004768589,0.0001847988,0.0006412812,0.0004459669,0.00009784268,0.0000883922,0.001040305,0.00004959582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00022064,"about_ca_system_score_gemma":0.0001508768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001171907,"about_ca_topic_score_gemma":0.00001812334,"domain_scores_codex":[0.9971459,0.00003572375,0.001464184,0.0005930161,0.0003200319,0.0004411488],"domain_scores_gemma":[0.9974567,0.001667765,0.0001508601,0.0003995714,0.00004126814,0.0002838658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005274819,0.0001023049,0.002555971,0.033723,0.000666486,0.000351774,0.00008385508,0.00000367974,0.000003833577,0.0007624188,0.02213983,0.9396016],"study_design_scores_gemma":[0.0007494698,0.00005419029,0.0001297101,0.01354455,0.001615865,0.0002789155,0.00004267076,0.0001103875,2.437513e-7,0.0003721448,0.9826834,0.0004184731],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006499825,0.9954953,0.0005228397,0.0000762312,0.00181494,0.001192122,0.0002374847,0.0002101167,0.0003859907],"genre_scores_gemma":[0.0002353181,0.996976,0.0001890434,0.00007454625,0.001239774,0.0002760731,0.0006000331,0.0001549137,0.0002542889],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9605436,"threshold_uncertainty_score":0.9997683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02303403460601282,"score_gpt":0.3271974200723141,"score_spread":0.3041633854663013,"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."}}