{"id":"W4283770935","doi":"10.1016/j.mri.2022.06.010","title":"Quantitative susceptibility-weighted imaging in presence of strong susceptibility sources: Application to hemorrhage","year":2022,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Susceptibility weighted imaging; Quantitative susceptibility mapping; Phase imaging; Intracerebral hemorrhage; Medicine; Nuclear medicine; Nuclear magnetic resonance; Magnetic resonance imaging; Radiology; Physics; Pathology; Surgery","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":[0.001772856,0.0008361528,0.0005266482,0.001744699,0.0003705765,0.001176149,0.0008656766,0.001007585,0.001064302],"category_scores_gemma":[0.004690323,0.0004279226,0.0002640764,0.001005939,0.0008716787,0.0007978266,0.0008399116,0.0009094935,0.0001893475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000224191,"about_ca_system_score_gemma":0.0004342005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006694561,"about_ca_topic_score_gemma":0.0006226668,"domain_scores_codex":[0.9997868,0.0001105776,0.00001602798,0.00003126795,0.00004176703,0.00001366676],"domain_scores_gemma":[0.9984362,0.001040231,0.0001221188,0.0001323005,0.0001699498,0.00009927467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002804323,0.0006073289,0.01423291,0.001499426,0.0001759319,0.01501485,0.0006013328,0.02199535,0.6141017,0.01332369,0.002366198,0.313277],"study_design_scores_gemma":[0.00059065,0.001730297,0.03631427,0.0002582222,0.0006314637,0.04164548,0.0007953893,0.5480242,0.3056352,0.05304994,0.01106605,0.0002588168],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2831524,0.00740299,0.6976355,0.001602469,0.00016537,0.0003178936,0.000271025,0.001233956,0.008218425],"genre_scores_gemma":[0.8036513,0.004251398,0.1895315,0.0001695461,0.0002885696,0.00007660073,0.00006393794,0.0001759571,0.001791269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001772856,"threshold_uncertainty_score":0.00937587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358556671285875,"score_gpt":0.3098153034901915,"score_spread":0.2962297367773328,"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."}}