{"id":"W4321020753","doi":"10.1109/fg57933.2023.10042502","title":"Pain Detection in Masked Faces during Procedural Sedation","year":2023,"lang":"en","type":"article","venue":"","topic":"Anesthesia and Sedative Agents","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Sedation; Artificial intelligence; Computer science; Medicine; Face (sociological concept); Receiver operating characteristic; Anesthesia; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002070834,0.00004882148,0.00007516952,0.0001257849,0.00003009091,0.000005393381,0.00001723505,0.0000330062,0.0001263406],"category_scores_gemma":[0.0001296571,0.00003905778,0.00001788133,0.0003118001,0.000008631334,0.00007096268,0.000006799757,0.00005063181,0.0001672777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003502944,"about_ca_system_score_gemma":0.00001493876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003439343,"about_ca_topic_score_gemma":0.00006773051,"domain_scores_codex":[0.9995543,0.00003763146,0.00009583699,0.0001055325,0.00009603882,0.000110701],"domain_scores_gemma":[0.9998568,0.00002079015,0.00001692782,0.00005217819,0.00001736969,0.00003595238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002881905,0.00009344961,0.6316933,0.0003018842,0.00002567174,0.000175325,0.002217613,0.00005426111,0.3389239,0.00005177777,0.00064693,0.02552764],"study_design_scores_gemma":[0.0006345491,0.0000706787,0.9457235,0.00003112068,0.000004680092,0.00001091385,0.001340555,0.006012897,0.04576512,0.00003993772,0.0003128168,0.00005326271],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976354,0.000002603146,0.0002299967,0.000957466,0.00001734398,0.0002027355,1.18813e-7,0.0001352317,0.0008190972],"genre_scores_gemma":[0.9940126,0.000006576021,0.00004743905,0.0002773687,0.0000281235,0.00002161823,0.00001623862,0.000006815551,0.005583237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3140301,"threshold_uncertainty_score":0.2150072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127268147643905,"score_gpt":0.2727144293892972,"score_spread":0.2514417479128581,"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."}}