{"id":"W2035159547","doi":"10.1158/1538-7445.am2012-4285","title":"Abstract 4285: Visualizing cancer vaccine clearance <i>in vivo</i> using magnetic resonance imaging","year":2012,"lang":"en","type":"article","venue":"Cancer Research","topic":"Nanoplatforms for cancer theranostics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Institute for Biodiagnostics; Immunovaccine (Canada)","funders":"","keywords":"Adjuvant; Biodistribution; Immunotherapy; Immune system; Medicine; Cancer vaccine; Context (archaeology); Magnetic resonance imaging; Cancer; Antigen; Vaccination; Cancer immunotherapy; Cytotoxic T cell; Cancer research; Immunology; In vivo; Internal medicine; Chemistry; Biology; In vitro; Radiology","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":[0.0002067157,0.0003873648,0.0001449366,0.000344865,0.0001577327,0.0003021919,0.0002213415,0.0004354554,0.002570223],"category_scores_gemma":[0.0001361735,0.0001446044,0.0001266714,0.0002033816,0.0001633329,0.0003118984,0.0001627162,0.0005090274,0.0006683299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002049377,"about_ca_system_score_gemma":0.0002398528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001098664,"about_ca_topic_score_gemma":0.0008073755,"domain_scores_codex":[0.9999154,0.00001769359,0.000004958904,0.00002008408,0.00002182428,0.00002008295],"domain_scores_gemma":[0.99993,0.00001803369,0.00001522255,0.000006669788,0.00001736431,0.00001262711],"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":[0.0000863362,0.0000189006,0.00005401097,0.00004204458,0.000002569402,0.00004614616,0.00001071442,0.00007524756,0.9978387,0.0001255544,0.0002546741,0.001445098],"study_design_scores_gemma":[0.00001418839,0.0002561088,0.001196855,0.00001192929,0.00001010974,0.0002459892,0.00001724021,0.001860823,0.9925868,0.00006534303,0.003728844,0.000005849722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910899,0.007500272,0.07479172,0.0009311972,0.0002352797,0.0002010244,0.0009836027,0.001172949,0.02309413],"genre_scores_gemma":[0.906469,0.003990482,0.06545375,0.0005488772,0.00008129169,0.0002372428,0.001495184,0.0002955396,0.02142871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002570223,"threshold_uncertainty_score":0.008598208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05411076212939193,"score_gpt":0.3744896363811363,"score_spread":0.3203788742517444,"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."}}