{"id":"W7162007708","doi":"10.82308/40058","title":"Utilizing AI and EEG to assess expertise and stress in virtual neurosurgical performance","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electroencephalography; EEG-fMRI; Behavioral analysis; Neuromarketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007701401,0.0005472033,0.0002880726,0.001223066,0.0001937655,0.001453337,0.0002856383,0.0006284674,0.001543924],"category_scores_gemma":[0.005058839,0.0001330651,0.0003010167,0.000619335,0.0004483327,0.0008703819,0.0007798865,0.0003288933,0.0002768101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003599759,"about_ca_system_score_gemma":0.0003769404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003354206,"about_ca_topic_score_gemma":0.00437298,"domain_scores_codex":[0.9995542,0.0001563007,0.00003661973,0.00009045319,0.0001117507,0.00005063069],"domain_scores_gemma":[0.9988152,0.0005719617,0.0002280378,0.00005603881,0.0001880427,0.0001408026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002208912,0.001196327,0.4106163,0.001149527,0.0006792407,0.0006251363,0.005318378,0.05035055,0.06826017,0.001908843,0.001431792,0.4562547],"study_design_scores_gemma":[0.00009290931,0.002020611,0.8601301,0.0002020732,0.0002605164,0.0007123713,0.00454856,0.1154284,0.009242564,0.005125797,0.002099616,0.0001364313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740571,0.0004209742,0.02050227,0.0001839853,0.00004010317,0.000174485,0.0003173534,0.00008862945,0.004215143],"genre_scores_gemma":[0.9917596,0.0002691517,0.006936544,0.000034135,0.00002540077,0.0001058128,0.000100193,0.000006617914,0.0007625837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003354206,"threshold_uncertainty_score":0.006669343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04850564045694791,"score_gpt":0.3553790894002273,"score_spread":0.3068734489432794,"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."}}