{"id":"W2119569230","doi":"10.1186/s12968-014-0055-3","title":"Optimized saturation recovery protocols for T1-mapping in the heart: influence of sampling strategies on precision","year":2014,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Cancer Institute; National Heart, Lung, and Blood Institute; NHLBI Division of Intramural Research; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Reproducibility; Monte Carlo method; Sampling (signal processing); Accuracy and precision; Standard deviation; Quality assurance; Range (aeronautics); Algorithm; Computer science; Curve fitting; Sensitivity (control systems); Statistics; Mathematics; Medicine; Materials science","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":[0.00315682,0.0001293352,0.0006638551,0.0001631402,0.00004292257,0.00006107126,0.0001361492,0.00006916148,0.000001438248],"category_scores_gemma":[0.003300323,0.00008787705,0.0007776081,0.0002262639,0.00005390975,0.0001534995,0.00001383036,0.0002681592,8.63734e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004340896,"about_ca_system_score_gemma":0.000163141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001100423,"about_ca_topic_score_gemma":3.275027e-7,"domain_scores_codex":[0.998079,0.0002648106,0.0006143372,0.0001564868,0.0007126719,0.0001727112],"domain_scores_gemma":[0.9976825,0.001174138,0.0002178072,0.0004352706,0.0004480619,0.00004219459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002431997,0.0002236234,0.00274572,0.0006572276,0.0002345792,0.00003468472,0.0008921233,0.427935,0.002650392,0.000292909,0.0009247818,0.5609769],"study_design_scores_gemma":[0.009809129,0.003154104,0.5849824,0.005541531,0.0004406874,0.0005406509,0.0004135307,0.002160123,0.00167264,0.001617836,0.3894164,0.0002509719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8677971,0.05352835,0.06440992,0.0008873011,0.0002970037,0.01178705,0.000007670994,0.00001715163,0.001268499],"genre_scores_gemma":[0.949456,0.001370432,0.0473943,0.0003757907,0.0005904874,0.0007623199,0.000002494788,0.00002871418,0.00001942831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5822367,"threshold_uncertainty_score":0.3951033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02421240847598027,"score_gpt":0.3008785081513183,"score_spread":0.2766660996753381,"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."}}