{"id":"W4408855460","doi":"10.2196/71844","title":"Knowledge Mapping and Global Trends in Simulation in Medical Education: Bibliometric and Visual Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Simulation-Based Education in Healthcare","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Visualization; Bibliometrics; Field (mathematics); Library science; Regional science; Data science; Medical education; Geography; Computer science; Medicine; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009314174,0.0005068098,0.001125851,0.1343647,0.0008228007,0.00498718,0.0006241198,0.0005167947,0.003154894],"category_scores_gemma":[0.04682451,0.0001943473,0.001462558,0.1633021,0.0008639843,0.00427934,0.002315947,0.000395129,0.0005411954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002125275,"about_ca_system_score_gemma":0.002389262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006040141,"about_ca_topic_score_gemma":0.004680385,"domain_scores_codex":[0.9916898,0.002094111,0.001447467,0.0006801728,0.003773653,0.0003148045],"domain_scores_gemma":[0.9512863,0.02957047,0.009280062,0.002005923,0.007320768,0.0005365508],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002865048,0.0001372767,0.6656894,0.005896013,0.0015181,0.0004006011,0.007504362,0.007472115,0.001872754,0.0101845,0.01025481,0.2887835],"study_design_scores_gemma":[0.00004571951,0.0001758174,0.9164799,0.001434405,0.0007636279,0.000764186,0.01147042,0.0210377,0.002016717,0.0102335,0.03546103,0.0001169355],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049698,0.0156765,0.009021721,0.002139217,0.0001180297,0.000409691,0.03269988,0.000831659,0.03413355],"genre_scores_gemma":[0.9778898,0.004494737,0.008327867,0.00005273809,0.0001258216,0.0002850511,0.007914696,0.00006217979,0.0008471974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9906858,"threshold_uncertainty_score":0.04925865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02838928675954572,"score_gpt":0.4921320060137204,"score_spread":0.4637427192541747,"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."}}