{"id":"W4224277908","doi":"10.2196/33842","title":"Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health informatics; Informatics; Public health informatics; Bibliometrics; Health care; Data science; Health Administration Informatics; The Internet; Library science; Medicine; Computer science; Public health; Political science; World Wide Web; Health policy; International health; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics","insufficient_payload"],"category_scores_codex":[0.006297391,0.0002119985,0.0005425047,0.02609497,0.0008473804,0.0002024302,0.0008723072,0.0003236842,0.0286779],"category_scores_gemma":[0.002820197,0.0001781647,0.0002038529,0.1301794,0.0003066222,0.0002532481,0.0006302006,0.001470585,0.003096951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005600946,"about_ca_system_score_gemma":0.002371499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006631684,"about_ca_topic_score_gemma":0.0002296424,"domain_scores_codex":[0.9880075,0.0002221797,0.001820924,0.0002385781,0.008857816,0.0008529741],"domain_scores_gemma":[0.9951392,0.000923634,0.0002325045,0.0008782693,0.0007135847,0.002112764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003175437,0.0009521007,0.06474955,0.0002107176,0.0003227057,0.00006665487,0.02069298,0.0002765617,0.000001071478,0.0004785113,0.6133844,0.2985472],"study_design_scores_gemma":[0.0009712787,0.003715194,0.07684287,0.0004258696,0.0005194295,0.0001828799,0.1265085,0.316619,0.0001619091,0.001794309,0.4712994,0.0009592677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796479,0.0001409863,0.001559544,0.009444728,0.001366058,0.0008264767,0.0001341294,0.0001133153,0.006766814],"genre_scores_gemma":[0.988206,0.0001173348,0.001285554,0.008061022,0.0006200728,0.0002952127,0.0008841627,0.00001589147,0.0005147854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3163425,"threshold_uncertainty_score":0.9976792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1692497379824417,"score_gpt":0.4944459984231761,"score_spread":0.3251962604407344,"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."}}