{"id":"W2900621170","doi":"10.1109/nssmic.2017.8533090","title":"Evaluation of SRW-OSEM Using Clinical Data","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Cancer Research UK","keywords":"Iterative reconstruction; Imaging phantom; Image quality; Computer science; Algorithm; Weighting; Convergence (economics); Rate of convergence; Artificial intelligence; Image (mathematics); Nuclear medicine; Physics; Medicine","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":[0.01159925,0.001027106,0.0006448212,0.001397501,0.000355951,0.001232286,0.001126045,0.001047481,0.002091998],"category_scores_gemma":[0.0267583,0.0003703259,0.0004705431,0.001050776,0.0005790879,0.0009758924,0.0008818872,0.0005735007,0.0007312535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004589597,"about_ca_system_score_gemma":0.0006152586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135517,"about_ca_topic_score_gemma":0.001395021,"domain_scores_codex":[0.9965708,0.001855393,0.0002551865,0.0003011347,0.0009100445,0.0001074035],"domain_scores_gemma":[0.9893468,0.005366896,0.0005795546,0.0009071013,0.003597519,0.0002021227],"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":[0.006557747,0.001151193,0.0196089,0.002776026,0.0006120714,0.001562007,0.00212376,0.2410394,0.2062156,0.004352595,0.005180181,0.5088205],"study_design_scores_gemma":[0.0002918622,0.00347488,0.01733504,0.0001790361,0.0001770404,0.002390353,0.0007192316,0.7039188,0.2568256,0.001109967,0.01340745,0.000170686],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6633641,0.002187152,0.3220528,0.0004208212,0.000170689,0.0008062248,0.00110226,0.005024387,0.004871628],"genre_scores_gemma":[0.6144349,0.0009292042,0.3787654,0.0001901171,0.00003036296,0.0003830466,0.001978556,0.001562415,0.00172594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01159925,"threshold_uncertainty_score":0.06134349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8449330269712352,"score_gpt":0.6986830526577659,"score_spread":0.1462499743134693,"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."}}