{"id":"W2006781331","doi":"10.1088/0031-9155/50/6/014","title":"Reassessing the clinical efficacy of two MR quantitative DSC PWI CBF algorithms following cross-calibration with PET images","year":2005,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Deconvolution; Singular value decomposition; Calibration; Algorithm; Cerebral blood flow; Mean transit time; Computer science; Ground truth; Magnetic resonance imaging; Perfusion scanning; Mathematics; Nuclear medicine; Perfusion; Artificial intelligence; Medicine; Statistics; 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":[0.01753691,0.0009748251,0.000859149,0.001296666,0.0004298091,0.001998774,0.0006970768,0.001647441,0.0006730266],"category_scores_gemma":[0.08762074,0.0005151518,0.0004034231,0.0006301606,0.001145916,0.001546961,0.0007191504,0.001203781,0.0004855025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007730552,"about_ca_system_score_gemma":0.0004351663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065251,"about_ca_topic_score_gemma":0.001046928,"domain_scores_codex":[0.9954881,0.002480772,0.0004288366,0.0006695246,0.0008043932,0.0001283956],"domain_scores_gemma":[0.9694282,0.02168074,0.001292872,0.002670697,0.004606916,0.0003206249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01554602,0.0006820356,0.04059182,0.0007983141,0.0008339048,0.0007078703,0.00207983,0.06847537,0.1401229,0.00444582,0.002322319,0.7233939],"study_design_scores_gemma":[0.001202335,0.01233306,0.1195815,0.0001761894,0.001141896,0.003762129,0.0007123424,0.5771964,0.2714174,0.004180863,0.007877159,0.0004186359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7333284,0.004161644,0.2546489,0.001837387,0.0003481383,0.0003488199,0.0002315572,0.001440187,0.003655049],"genre_scores_gemma":[0.9031274,0.0006005173,0.09440451,0.0003200063,0.000122916,0.0001500351,0.0001841492,0.000336126,0.0007542245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01753691,"threshold_uncertainty_score":0.09274513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2737178765336788,"score_gpt":0.5612943477158667,"score_spread":0.2875764711821879,"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."}}