{"id":"W2954050745","doi":"10.1158/1538-7445.am2019-1138","title":"Abstract 1138: Non-invasive detection and quantification of tumor-associated macrophage density with magnetic particle imaging","year":2019,"lang":"en","type":"article","venue":"Cancer Research","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Magnetic particle imaging; Breast cancer; Magnetic resonance imaging; In vivo; Cancer; Preclinical imaging; Medicine; Magnetic nanoparticles; Pathology; Cancer research; Chemistry; Nuclear medicine; Materials science; Biology; Internal medicine; Radiology; Nanoparticle; Nanotechnology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002310057,0.00006706488,0.00009536368,0.0000643634,0.00006086808,0.00004156051,0.0000747637,0.00002122894,0.0001399557],"category_scores_gemma":[0.00002994886,0.00006484174,0.00001162179,0.0003334933,0.00008527788,0.0000968694,0.00002099332,0.0001221246,0.00004118264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006092961,"about_ca_system_score_gemma":0.00003284801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00012961,"about_ca_topic_score_gemma":0.0001253226,"domain_scores_codex":[0.9992834,0.00002214012,0.0001425068,0.0001565532,0.000199121,0.0001962626],"domain_scores_gemma":[0.9994618,0.00007975294,0.00003131123,0.0001824956,0.00018301,0.00006158792],"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":[0.00002278676,0.00002255545,0.01713716,0.00008646848,0.000005800971,0.000001274372,0.0001106108,0.0005352653,0.9733112,0.00002594334,0.00002003834,0.008720824],"study_design_scores_gemma":[0.00031274,0.00003334721,0.3062594,0.00004190534,0.000004433797,0.000001408946,0.0001160593,0.01017379,0.6829276,0.00002665041,0.00004150026,0.00006113829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988139,0.0002947731,0.0002210152,0.00009997472,0.00003137984,0.0003251552,0.000008917333,0.00004888004,0.0001559589],"genre_scores_gemma":[0.9996232,0.000169742,0.00003534806,0.00000771379,0.00001345505,0.00006697251,0.000003634515,0.00001712745,0.00006285554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2903837,"threshold_uncertainty_score":0.264417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757283354857027,"score_gpt":0.2774166073420863,"score_spread":0.2598437737935161,"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."}}