{"id":"W4386083791","doi":"10.1093/asj/sjad277","title":"Medical Applications of Artificial Intelligence and Large Language Models: Bibliometric Analysis and Stern Call for Improved Publishing Practices","year":2023,"lang":"en","type":"article","venue":"Aesthetic Surgery Journal","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University Health Centre","funders":"","keywords":"Medicine; Stern; Publishing; MEDLINE; Data science; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.09295917,0.0008333641,0.002576245,0.06814034,0.001069097,0.01271474,0.001764913,0.002004347,0.00288821],"category_scores_gemma":[0.262342,0.000551493,0.001839303,0.119489,0.003374824,0.01482855,0.003690952,0.002179499,0.0006919509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004463657,"about_ca_system_score_gemma":0.006397889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004764117,"about_ca_topic_score_gemma":0.005546326,"domain_scores_codex":[0.9429873,0.02828485,0.006382061,0.003222144,0.01847809,0.0006456925],"domain_scores_gemma":[0.646362,0.2579639,0.03676894,0.0238856,0.03201908,0.003000421],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001843493,0.0001205923,0.2416492,0.006382179,0.002826816,0.000186394,0.002928134,0.004658909,0.0004285761,0.05147381,0.06121511,0.627946],"study_design_scores_gemma":[0.0001341378,0.00027887,0.4188845,0.01356421,0.001518925,0.001534578,0.01133779,0.03443632,0.001434108,0.2462731,0.2701726,0.0004308063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.170142,0.355709,0.04783579,0.367716,0.002408991,0.0004984587,0.01131428,0.001053708,0.04332174],"genre_scores_gemma":[0.7859547,0.1409857,0.04338464,0.01181567,0.008346966,0.0005411658,0.007172083,0.0002671324,0.001531974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9318597,"threshold_uncertainty_score":0.491621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2076754024591441,"score_gpt":0.4414365580445385,"score_spread":0.2337611555853945,"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."}}