{"id":"W4319786641","doi":"10.1016/j.bjps.2023.01.039","title":"Artificial intelligence-enabled simulation of gluteal augmentation: A helpful tool in preoperative outcome simulation?","year":2023,"lang":"en","type":"article","venue":"Journal of Plastic Reconstructive & Aesthetic Surgery","topic":"Body Contouring and Surgery","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Outcome (game theory); Lift (data mining); Informed consent; Computer science; Generative adversarial network; Key (lock); Artificial intelligence; Surgery; Medical physics; Medicine; Deep learning; Machine learning; Computer security; Alternative 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":[0.00165641,0.0002137547,0.0009390669,0.001209646,0.00006462787,0.00002975553,0.0000713984,0.0001309369,0.0001744627],"category_scores_gemma":[0.002765817,0.0001837746,0.0004166559,0.0009282895,0.0001762776,0.0003068143,0.00002201815,0.0003578366,0.00002580255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001882163,"about_ca_system_score_gemma":0.0005551234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009487573,"about_ca_topic_score_gemma":0.000004017271,"domain_scores_codex":[0.9966068,0.0002515586,0.002064994,0.000224922,0.000554275,0.0002975069],"domain_scores_gemma":[0.9926746,0.005474256,0.0009009226,0.0001650841,0.0006804276,0.0001047068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001585464,0.0001907096,0.5227247,0.00008194357,0.0001266823,0.000266407,0.001180529,0.402344,0.0001548202,0.0001635314,0.0001048344,0.07107634],"study_design_scores_gemma":[0.001531741,0.0007768688,0.5225537,0.002206728,0.0004031458,0.0007970872,0.00575004,0.4578684,0.001920635,0.005434573,0.0001079553,0.000649152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896007,0.00004698352,0.008309088,0.0002082391,0.001453926,0.0002470363,0.000008476019,0.00002524622,0.0001003651],"genre_scores_gemma":[0.9991986,0.00002226013,0.0003633499,0.00002358936,0.0002910341,0.000007045401,0.000007797693,0.00002311286,0.00006318013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07042719,"threshold_uncertainty_score":0.7494112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06910553722293686,"score_gpt":0.3373527795377332,"score_spread":0.2682472423147964,"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."}}