{"id":"W2516901005","doi":"10.1016/j.mri.2016.08.023","title":"Hair product artifact in magnetic resonance imaging","year":2016,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Facial Rejuvenation and Surgery Techniques","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Women and Children’s Health Research Institute; University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Artifact (error); Cosmetics; Magnetic resonance imaging; Nuclear magnetic resonance; Metal; Materials science; Chemistry; Computer science; Medicine; Radiology; Computer vision; Metallurgy; Organic chemistry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006795377,0.0003341154,0.0004720291,0.0004273429,0.00009229056,0.00006291833,0.000221008,0.00004832498,0.0009453453],"category_scores_gemma":[0.0009018406,0.0002573351,0.0001286336,0.0006130888,0.0002999288,0.0003282053,0.00009674529,0.0002693864,0.0001889278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002225217,"about_ca_system_score_gemma":0.0001771737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001575609,"about_ca_topic_score_gemma":0.00003355741,"domain_scores_codex":[0.9970955,0.0001128574,0.0006688858,0.0007858719,0.0005562481,0.0007806724],"domain_scores_gemma":[0.9986624,0.0001622087,0.00009719138,0.0007184168,0.0001789505,0.000180824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008614566,0.00006736649,0.2926887,0.00001443659,4.193576e-7,0.0002447577,0.00007697305,1.020816e-7,0.01500033,0.0002272442,0.007412466,0.6841811],"study_design_scores_gemma":[0.001027766,0.00006004671,0.4454619,0.0006788622,0.000006915602,0.00010689,0.00002941929,0.000551344,0.006384376,0.0006084104,0.5448188,0.0002652255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4796698,0.2848564,0.001395919,0.1638045,0.001210797,0.003279349,0.00004871229,0.001808637,0.06392591],"genre_scores_gemma":[0.9774048,0.001019751,0.002932839,0.003649123,0.0001144793,0.0001634105,0.000004833865,0.0000774659,0.01463332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6839159,"threshold_uncertainty_score":0.9999879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162799825130503,"score_gpt":0.2655049109030923,"score_spread":0.2538769126517872,"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."}}