{"id":"W6964938717","doi":"10.3389/fnagi.2023.1238274.s004","title":"Table_1_Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes.docx","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Plant Ecology and Soil Science","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cognition; Electroencephalography; Montreal Cognitive Assessment; Stroke (engine); Lateralization of brain function; Lesion; Laterality","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003122813,0.0003685702,0.0004416776,0.0001507558,0.0004333803,0.00004021299,0.0005093916,0.0004412589,0.08121026],"category_scores_gemma":[0.00203996,0.0003735351,0.0001698197,0.0009722929,0.0001409901,0.0002133408,0.0003565303,0.0008919781,0.003374547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001660558,"about_ca_system_score_gemma":0.0001739802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001029745,"about_ca_topic_score_gemma":0.002039413,"domain_scores_codex":[0.9970588,0.0002962191,0.0003923289,0.0007117161,0.0005484489,0.0009924739],"domain_scores_gemma":[0.9979495,0.0009789112,0.0005367654,0.0002647878,0.00006539599,0.0002045768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000495488,0.00005325828,0.002633964,0.00006034743,0.00003654508,0.00005413546,0.00001039891,0.03625141,0.0002893961,3.35497e-8,0.9604205,0.000140476],"study_design_scores_gemma":[0.001441446,0.001913939,0.2739223,0.002760594,0.0002556166,0.00004130126,0.00004913783,0.05015128,0.0004284465,0.00003069342,0.6677829,0.001222394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002295376,0.00007230522,0.00001144662,0.0000272151,0.0001841501,0.0004344714,0.9968351,0.0001004346,0.00003952852],"genre_scores_gemma":[0.002363329,0.00002198207,0.00003669964,0.0003044629,0.0001282203,0.0001007096,0.996937,0.0000224491,0.00008510089],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2926376,"threshold_uncertainty_score":0.9998717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02142441736315729,"score_gpt":0.2441718150053278,"score_spread":0.2227473976421705,"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."}}