{"id":"W4289755266","doi":"10.1007/s10877-022-00897-z","title":"Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Monitoring and Computing","topic":"Anesthesia and Pain Management","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Polytechnique Montréal; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Hôpital Maisonneuve-Rosemont","funders":"","keywords":"Medicine; Anesthesia; Nociception; Fentanyl; Intubation; Area under the curve; Anesthesiology; Internal medicine","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":[0.006319555,0.0001173796,0.0005907549,0.0002285447,0.0002156488,0.0000138871,0.00009877699,0.00004884364,0.00002139271],"category_scores_gemma":[0.0004715793,0.00007241458,0.0004938091,0.0003997106,0.00007545076,0.00003248748,0.00002908277,0.0007997426,1.052898e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007369516,"about_ca_system_score_gemma":0.0001353485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006439666,"about_ca_topic_score_gemma":2.372635e-7,"domain_scores_codex":[0.996778,0.001549277,0.0009552817,0.0001351043,0.0004723297,0.0001100431],"domain_scores_gemma":[0.9968168,0.001772316,0.0009463846,0.0001215966,0.0002671614,0.00007576509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007592803,0.0002141363,0.6156759,0.00001236778,0.0009336727,0.00001124122,0.0004410133,0.3738249,0.00009224023,0.000001534363,0.00001908702,0.008014637],"study_design_scores_gemma":[0.0009715776,0.004557551,0.5218835,0.0001003554,0.0005646429,0.000003617943,0.0005803899,0.471228,0.00005756602,0.000004875711,0.00001597799,0.00003190988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639158,0.00006109894,0.0351636,0.0005967252,0.0001012514,0.0001280303,0.000004548444,0.000007362825,0.00002155464],"genre_scores_gemma":[0.9988699,0.00002106071,0.0006201559,0.0001556585,0.0002900806,0.000001322536,0.000006976034,0.000008558864,0.00002631763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09740318,"threshold_uncertainty_score":0.3474528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0659688429819344,"score_gpt":0.3811007058604289,"score_spread":0.3151318628784945,"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."}}