{"id":"W2141282606","doi":"10.1002/jmri.21357","title":"Parallel imaging enhanced MR colonography using a phantom model","year":2008,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Radiological Society of North America","keywords":"Imaging phantom; Image quality; Nuclear medicine; Medicine; Image resolution; Materials science; Biomedical engineering; Radiology; Computer science; Artificial intelligence; Image (mathematics)","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":[0.001061468,0.001208587,0.0005849077,0.0006322237,0.0002310531,0.0004456479,0.0007042932,0.001119309,0.002823088],"category_scores_gemma":[0.001411167,0.0007067467,0.0005912703,0.0006902346,0.0003878762,0.0004771977,0.0004411144,0.0006303291,0.0008738109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004242348,"about_ca_system_score_gemma":0.0005609427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001586592,"about_ca_topic_score_gemma":0.001137854,"domain_scores_codex":[0.9993906,0.0001730646,0.00004261053,0.0001954951,0.0001485002,0.00004976576],"domain_scores_gemma":[0.998673,0.0005341209,0.0002393892,0.0002805477,0.000192612,0.00008034209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003228123,0.000913505,0.0006333666,0.000541491,0.00006385524,0.0005214015,0.0001094803,0.00993511,0.9773073,0.0003671841,0.0003360259,0.006043185],"study_design_scores_gemma":[0.001490899,0.03619528,0.01287641,0.0001613721,0.000856608,0.008430777,0.0001435871,0.08411929,0.8320718,0.0006206082,0.02283499,0.0001984454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8433436,0.002376163,0.1452496,0.0003907115,0.0001972696,0.001687334,0.001944081,0.0008998734,0.003911354],"genre_scores_gemma":[0.816209,0.002674897,0.1685929,0.0003700736,0.00004710866,0.001582865,0.004018938,0.0001977049,0.006306466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002823088,"threshold_uncertainty_score":0.009444177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311726287195422,"score_gpt":0.2816586143328272,"score_spread":0.258541351460873,"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."}}