{"id":"W3092145373","doi":"10.1016/j.neuroimage.2020.117439","title":"Cortico-spinal imaging to study pain","year":2020,"lang":"en","type":"review","venue":"NeuroImage","topic":"Pain Mechanisms and Treatments","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Deutsche Forschungsgemeinschaft; Max-Planck-Gesellschaft; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Bundesministerium für Bildung und Forschung","keywords":"Spinal cord; Functional magnetic resonance imaging; Magnetic resonance imaging; Neuroscience; Neuroimaging; Perception; Medicine; Psychology; Computer science; Physical medicine and rehabilitation; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003263583,0.0004407519,0.00154789,0.0001414497,0.00007262085,0.00004814138,0.0001743851,0.0000507504,0.0001097976],"category_scores_gemma":[0.0003874881,0.0003428648,0.0003514621,0.0003442628,0.00001340245,0.00002739529,0.000139112,0.0003992725,0.0007868735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008912183,"about_ca_system_score_gemma":0.0001441481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001070063,"about_ca_topic_score_gemma":6.115473e-7,"domain_scores_codex":[0.9977682,0.0004139709,0.0004522868,0.0007057676,0.0003430366,0.0003167427],"domain_scores_gemma":[0.9988182,0.00008352086,0.0001376506,0.0005584939,0.00003481955,0.0003673804],"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":[0.00001951361,0.000407567,0.00009583314,0.00217457,0.0001310711,0.01156066,0.00003351672,5.575142e-9,0.00000625278,0.000006582356,0.0006662465,0.9848982],"study_design_scores_gemma":[0.0005826326,0.001648773,0.0003167749,0.002704928,0.002000776,0.0002485208,0.00002500194,0.00001056806,0.000001099968,0.000006439723,0.9922217,0.0002328473],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002708652,0.9951687,0.0005476351,0.0002513959,0.0002121688,0.002860422,0.00002128663,0.0001587069,0.0007526021],"genre_scores_gemma":[0.0004427291,0.9950139,0.001016487,0.002375674,0.0002890761,0.00025424,0.00004517028,0.0001530723,0.0004096669],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9915554,"threshold_uncertainty_score":0.9999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07626273536874832,"score_gpt":0.3904932637660767,"score_spread":0.3142305283973283,"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."}}