{"id":"W2170864001","doi":"10.1002/jmri.20996","title":"Artifacts and pitfalls in MR imaging of the pelvis","year":2007,"lang":"en","type":"review","venue":"Journal of Magnetic Resonance Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Mount Sinai Hospital; University of Toronto; University Health Network; McGill University; McGill University Health Centre; Montreal General Hospital","funders":"","keywords":"Troubleshooting; Artifact (error); Computer science; Radiology; Medical physics; Medicine; Artificial intelligence","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":[0.001501407,0.0003704101,0.001923111,0.000587388,0.00003666132,0.00004149496,0.0003989814,0.0000951273,0.000120166],"category_scores_gemma":[0.0004526504,0.0002423223,0.0005032987,0.0006390682,0.000284492,0.0001491174,0.0001539296,0.001155247,0.000003876422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002982516,"about_ca_system_score_gemma":0.0006513618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004903793,"about_ca_topic_score_gemma":0.00001058799,"domain_scores_codex":[0.9965618,0.0001763398,0.001781808,0.0002930958,0.0007626391,0.0004242687],"domain_scores_gemma":[0.9970101,0.000625429,0.001466269,0.0004869812,0.0002529686,0.0001582476],"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":[0.00002182108,0.00006109343,0.02688694,0.004772147,0.00001355853,0.0003482095,0.0001200712,5.004605e-7,0.000007007464,0.000007825843,0.002516053,0.9652448],"study_design_scores_gemma":[0.0008432162,0.00006914522,0.02124174,0.07296958,0.0006101467,0.002531879,0.00005766234,0.00002988732,0.00002087218,0.00005277675,0.9013805,0.0001926078],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005299617,0.9957811,0.00001252294,0.001622584,0.000449086,0.0005109128,0.000006245642,0.00000499763,0.001082605],"genre_scores_gemma":[0.000613615,0.9971731,0.001280222,0.0003384704,0.0003385585,0.000008264336,6.164086e-7,0.00006050331,0.0001866222],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9650522,"threshold_uncertainty_score":0.9881616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0372436457728851,"score_gpt":0.3527778125727262,"score_spread":0.3155341667998411,"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."}}