{"id":"W3100033656","doi":"10.1101/2020.07.13.200972","title":"An interactive meta-analysis of MRI biomarkers of myelin","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Université de Montréal; Polytechnique Montréal","funders":"Wellcome Trust","keywords":"Myelin; Relaxometry; Modalities; Meta-analysis; Computer science; Pathology; Magnetic resonance imaging; Psychology; Neuroscience; Medicine; Radiology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03740573,0.003438179,0.009720578,0.007792081,0.0009404151,0.004178252,0.002689268,0.002453694,0.009815715],"category_scores_gemma":[0.1064038,0.001356662,0.05560455,0.007197167,0.0007351671,0.001937014,0.002616253,0.002567259,0.0007952743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559225,"about_ca_system_score_gemma":0.002030803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006405499,"about_ca_topic_score_gemma":0.008385285,"domain_scores_codex":[0.9552743,0.03204942,0.003994217,0.005357138,0.00249568,0.0008292156],"domain_scores_gemma":[0.8998068,0.0870143,0.00443993,0.005510394,0.002621643,0.0006069221],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.002341273,0.0000187531,0.0118652,0.03070408,0.9438474,0.0001778518,0.00006926809,0.001628355,0.0005882582,0.0003725288,0.001668354,0.006718748],"study_design_scores_gemma":[0.0005826819,0.0001869867,0.007123287,0.001724415,0.9850211,0.000124277,0.00003694578,0.001562675,0.0003175005,0.00105517,0.002230091,0.00003488326],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.07602528,0.8277477,0.05660209,0.004279233,0.002248615,0.001176209,0.02682392,0.002147075,0.002949818],"genre_scores_gemma":[0.9101781,0.04503692,0.03017466,0.002011561,0.0008211914,0.002341042,0.006925909,0.000944571,0.001566169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9625943,"threshold_uncertainty_score":0.1978227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08975004590109842,"score_gpt":0.3442505196969797,"score_spread":0.2545004737958813,"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."}}