{"id":"W2274250433","doi":"10.3978/j.issn.2223-4292.2016.01.05","title":"Arterial input function placement effect on computed tomography lung perfusion maps.","year":2016,"lang":"en","type":"article","venue":"PubMed","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Sunnybrook Health Science Centre","funders":"University of Toronto","keywords":"Perfusion; Rank correlation; Spearman's rank correlation coefficient; Medicine; Nuclear medicine; Perfusion scanning; Lung; Analysis of variance; Linear regression; Radiology; Mathematics; Internal medicine; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003748325,0.0006990057,0.0004092322,0.0006937073,0.0002358793,0.001066616,0.000351988,0.0006406558,0.0009935711],"category_scores_gemma":[0.02063484,0.0003172813,0.0002583838,0.0004770501,0.0005796932,0.0006774944,0.0004486422,0.0005821967,0.0003964049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006949526,"about_ca_system_score_gemma":0.0004538718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00131448,"about_ca_topic_score_gemma":0.000903569,"domain_scores_codex":[0.9983012,0.0007127908,0.0001109517,0.0003135494,0.0004634531,0.00009810202],"domain_scores_gemma":[0.979107,0.01636721,0.001948232,0.000889043,0.001452979,0.000235444],"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":[0.00709796,0.0002110573,0.2655965,0.0007556687,0.0003874248,0.001813007,0.0006283921,0.0252551,0.4723322,0.0005705861,0.0007519205,0.2246001],"study_design_scores_gemma":[0.00006274517,0.002384129,0.4479213,0.00008664731,0.0005611048,0.00622192,0.0001858942,0.1016955,0.4371102,0.0008009117,0.002867951,0.0001017705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8897871,0.001996432,0.1054842,0.0001374295,0.00007715864,0.00008161406,0.0002781288,0.000816461,0.001341606],"genre_scores_gemma":[0.9824034,0.0002745126,0.01675191,0.00003415043,0.00001833164,0.00003092944,0.0001435358,0.0001176005,0.0002255225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003748325,"threshold_uncertainty_score":0.01982331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169180027571143,"score_gpt":0.2273645438531536,"score_spread":0.2156727435774421,"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."}}