{"id":"W2070991200","doi":"10.1002/jmri.22681","title":"First order correction for T<sub>2</sub>*‐relaxation in determining contrast agent concentration from spoiled gradient echo pulse sequence signal intensity","year":2011,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek","keywords":"Pulse sequence; Contrast (vision); Nuclear magnetic resonance; Echo (communications protocol); SIGNAL (programming language); Gradient echo; Intensity (physics); Sequence (biology); Pulse (music); Relaxation (psychology); Materials science; Physics; Chemistry; Optics; Magnetic resonance imaging; Medicine; Computer science; Radiology; Internal medicine","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":[0.001533627,0.0008985795,0.0004257129,0.000416452,0.0003931747,0.0008546514,0.001022245,0.001112488,0.00111561],"category_scores_gemma":[0.008572556,0.0004774609,0.0004321939,0.0003510724,0.0005899306,0.001017324,0.0005369536,0.00112731,0.0006860283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000157,"about_ca_system_score_gemma":0.001654879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003353635,"about_ca_topic_score_gemma":0.00527738,"domain_scores_codex":[0.9994022,0.000150815,0.00003380686,0.0001042076,0.0002675814,0.00004143392],"domain_scores_gemma":[0.9965867,0.002159996,0.0004094731,0.0003519629,0.000425826,0.00006609184],"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":[0.001016353,0.0003361237,0.01288554,0.00151593,0.0003246787,0.0007154818,0.0008506785,0.234424,0.4425622,0.01097199,0.002459031,0.2919379],"study_design_scores_gemma":[0.00004850244,0.0002305327,0.004246501,0.0000615821,0.0001298514,0.0010772,0.00004653233,0.7166727,0.2720767,0.001836354,0.003482027,0.00009153051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09952621,0.001351422,0.8961854,0.0002239219,0.0001224379,0.00008874683,0.00006258828,0.0013921,0.001047145],"genre_scores_gemma":[0.4011514,0.001428474,0.5926095,0.000242087,0.00004673455,0.0001537701,0.0001482065,0.0005688065,0.003650992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003353635,"threshold_uncertainty_score":0.008110702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02861833024875819,"score_gpt":0.2605299471514004,"score_spread":0.2319116169026422,"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."}}