{"id":"W7110829446","doi":"10.3389/feduc.2025.1610465.s011","title":"Table 2_Trends in neurology medical education: a bibliometric analysis (2000–2023).xlsx","year":2025,"lang":"","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bibliometrics; Impact factor; Neurology; Publishing; Table of contents; Web of science; MEDLINE; Trend analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["insufficient_payload"],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","bibliometrics","research_integrity","insufficient_payload"],"category_scores_codex":[0.001260337,0.002237295,0.004165034,0.6595162,0.0004108527,0.0009006908,0.007196561,0.00509759,0.9804544],"category_scores_gemma":[0.0885767,0.002643467,0.001324826,0.9033484,0.0001140185,0.0007720857,0.004089454,0.006470171,0.1081304],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000015,"about_ca_system_score_gemma":0.04530497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002519697,"about_ca_topic_score_gemma":0.01376416,"domain_scores_codex":[0.9819437,0.002204096,0.003507575,0.00459132,0.004668699,0.003084592],"domain_scores_gemma":[0.9849917,0.003174996,0.002109858,0.005709269,0.001932937,0.002081191],"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":[0.000170721,0.003426352,0.0008717288,0.002149055,0.002511933,0.001204468,0.0000207463,0.0002359209,9.143685e-8,0.000001362665,0.9799409,0.009466726],"study_design_scores_gemma":[0.001381254,0.000250799,0.02401981,0.005113894,0.003113149,0.0001065485,0.00001533866,0.002444295,0.000001036427,0.000005945265,0.9618762,0.001671798],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001094862,0.0330439,1.784973e-7,0.000959285,0.001224359,0.001001392,0.9606228,0.000161072,0.002976049],"genre_scores_gemma":[0.0001398447,0.0009510182,0.00001566704,0.004261549,0.001173164,0.00412404,0.9834079,0.0001740333,0.005752798],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.872324,"threshold_uncertainty_score":0.9990367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140698177150748,"score_gpt":0.3426720156086018,"score_spread":0.3112650338370943,"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."}}