{"id":"W4306194366","doi":"10.21203/rs.3.rs-2152608/v1","title":"Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; Erasmus+; Trent University; Nottingham Trent University","keywords":"Arousal; Anxiety; Virtual Reality Exposure Therapy; Biofeedback; Context (archaeology); Distress; Psychology; Virtual reality; Computer science; Cognitive psychology; Artificial intelligence; Clinical psychology; Social psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004346008,0.0009807555,0.0009566269,0.00188218,0.001162439,0.0004179489,0.002833952,0.0008594825,0.006627396],"category_scores_gemma":[0.002318412,0.0009211914,0.0003263724,0.001986133,0.0003128012,0.0002593264,0.003458239,0.006903073,0.0004099023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082228,"about_ca_system_score_gemma":0.0008214498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02094407,"about_ca_topic_score_gemma":0.0005205787,"domain_scores_codex":[0.9826696,0.007862195,0.0009487018,0.003831809,0.003249835,0.001437842],"domain_scores_gemma":[0.99109,0.002545074,0.0006342879,0.003994917,0.00123568,0.0005000536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00728134,0.006063179,0.1849016,0.0003239746,0.00187438,0.000319118,0.005890061,0.007449727,0.000589288,0.00001203692,0.006379011,0.7789163],"study_design_scores_gemma":[0.02102731,0.01278577,0.6024264,0.0009058063,0.0002772757,0.0000177412,0.006978149,0.1272503,0.002608246,0.00207708,0.2202,0.003446006],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9289606,0.008561989,0.01547117,0.001080417,0.005172801,0.006335836,0.02585321,0.001843417,0.006720526],"genre_scores_gemma":[0.9647008,0.01492647,0.0007797867,0.0001135133,0.001049023,0.001356528,0.01568205,0.000291651,0.001100224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7754703,"threshold_uncertainty_score":0.9993238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1934941510392416,"score_gpt":0.4629559162598099,"score_spread":0.2694617652205683,"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."}}