{"id":"W4313588533","doi":"10.3389/fneur.2022.1045678","title":"Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations","year":2023,"lang":"en","type":"review","venue":"Frontiers in Neurology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; National Institute on Aging; Deutsche Forschungsgemeinschaft; Marga und Walter Boll-Stiftung; Centre d'Imagerie BioMédicale; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institutes of Health; National Science Foundation","keywords":"Harmonization; Standardization; Computer science; Medical physics; Task (project management); Data science; Sample (material); Artificial intelligence; Data mining; Medicine; Systems engineering; Engineering; Chemistry","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":[0.1494809,0.001186469,0.004398438,0.004918805,0.0008921144,0.004839502,0.006872456,0.00305732,0.003202109],"category_scores_gemma":[0.09656402,0.0006527985,0.00420838,0.006889492,0.002484191,0.004663042,0.005622699,0.003197353,0.001462719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002798251,"about_ca_system_score_gemma":0.02784966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004281671,"about_ca_topic_score_gemma":0.003743699,"domain_scores_codex":[0.9547071,0.02439828,0.01004644,0.002710493,0.00710052,0.001037096],"domain_scores_gemma":[0.9182046,0.04034948,0.00860804,0.009598105,0.02182037,0.001419443],"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":[0.0003661397,0.0001097874,0.001755704,0.06338537,0.002057237,0.0002666534,0.001077157,0.001879829,0.0009466151,0.03437205,0.06914488,0.8246386],"study_design_scores_gemma":[0.0002226105,0.0002196847,0.00432564,0.06259397,0.001728435,0.0008890377,0.0006958081,0.0008255128,0.001160083,0.03037058,0.8968625,0.0001061379],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00254841,0.8966821,0.03444627,0.04398786,0.005810842,0.003228116,0.002694982,0.000274621,0.01032675],"genre_scores_gemma":[0.03173741,0.7644635,0.1378436,0.02704085,0.005174316,0.01563548,0.01241072,0.0005540311,0.005140035],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1494809,"threshold_uncertainty_score":0.79054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04031368821447522,"score_gpt":0.3404257871797219,"score_spread":0.3001120989652467,"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."}}