{"id":"W4392648865","doi":"10.1002/nbm.5139","title":"Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization‐prepared rapid gradient‐echo","year":2024,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary; University of Alberta","funders":"Canadian Institutes of Health Research; Consortium canadien en neurodégénérescence associée au vieillissement; Fondation Brain Canada","keywords":"Quantitative susceptibility mapping; Nuclear magnetic resonance; Gradient echo; Magnetic resonance imaging; Voxel; Spin echo; Magnetic susceptibility; Nuclear medicine; Materials science; Chemistry; Medicine; Physics; Radiology","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":[0.0009260415,0.0004185977,0.000198959,0.0004350471,0.00007869126,0.0003343124,0.0002253276,0.0003069824,0.0004275193],"category_scores_gemma":[0.002830317,0.0002245846,0.0002039025,0.0001389754,0.0002424288,0.0003543401,0.0002223316,0.00014194,0.00008937039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001678564,"about_ca_system_score_gemma":0.0002155167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006173863,"about_ca_topic_score_gemma":0.0008945291,"domain_scores_codex":[0.9998701,0.00006746805,0.000007255816,0.00001645383,0.00003201712,0.00000672021],"domain_scores_gemma":[0.9994357,0.0003936026,0.00007400815,0.00003079422,0.00005353099,0.00001234323],"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":[0.000557096,0.00006262297,0.006374764,0.0006580966,0.0001847765,0.0003607035,0.0001295617,0.1076787,0.8303947,0.000808309,0.0002967879,0.05249382],"study_design_scores_gemma":[0.0000732882,0.0007139466,0.01998857,0.00004235455,0.00013408,0.0008426845,0.00005909745,0.6043047,0.3711454,0.001993027,0.0006154908,0.00008750636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6630585,0.0008719824,0.334638,0.0001105473,0.00001660723,0.0000935915,0.0001416553,0.0006099363,0.0004592442],"genre_scores_gemma":[0.9262384,0.0002549159,0.07319678,0.000032088,0.00000651584,0.00005394925,0.00005414357,0.00003067563,0.0001324949],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009260415,"threshold_uncertainty_score":0.004897475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0946224563497522,"score_gpt":0.3850714892476286,"score_spread":0.2904490328978764,"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."}}