{"id":"W2314422247","doi":"10.1093/neuonc/nou264.8","title":"NI-08 * OPTIMIZATION OF MOLECULAR MR IMAGING OF GLIOMA","year":2014,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Alberta; University of Calgary","funders":"","keywords":"Glioma; Pulse sequence; Magnetic resonance imaging; Gradient echo; Contrast (vision); Pulse (music); Nuclear medicine; Spin echo; Medicine; Nuclear magnetic resonance; Radiology; Physics; Cancer research; Optics","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":[0.0002152536,0.0003785457,0.0001739709,0.0001588483,0.0001099092,0.0002806326,0.0002439645,0.0002673344,0.001579318],"category_scores_gemma":[0.0002312157,0.0001410157,0.0001208935,0.0001707221,0.0001105878,0.0001618977,0.0001931207,0.0003124561,0.0007050739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003166454,"about_ca_system_score_gemma":0.0002914117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001087648,"about_ca_topic_score_gemma":0.001683898,"domain_scores_codex":[0.9999011,0.0000227094,0.000005044009,0.00002221259,0.0000355076,0.0000135246],"domain_scores_gemma":[0.9999222,0.00001125419,0.00001901405,0.000005007469,0.00003071521,0.00001170693],"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":[0.000260292,0.00005101052,0.0004650547,0.000153653,0.000008672811,0.00004577336,0.00001866328,0.001282579,0.9845712,0.0002881892,0.0003840644,0.01247066],"study_design_scores_gemma":[0.00002337822,0.0005369195,0.001604663,0.00001186945,0.00002469972,0.0002570179,0.00002111307,0.00769346,0.9811562,0.00007538375,0.008582732,0.00001250402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9085128,0.005399083,0.06214442,0.0004817154,0.00008080232,0.0002706831,0.000559423,0.0007998545,0.02175131],"genre_scores_gemma":[0.882632,0.001802779,0.1041694,0.000178708,0.00001680438,0.0002290853,0.0009463416,0.0002369165,0.00978789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001579318,"threshold_uncertainty_score":0.005283356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008974620497344847,"score_gpt":0.2496329735080082,"score_spread":0.2406583530106634,"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."}}