{"id":"W4213126413","doi":"10.2196/25238","title":"International Technologies on Prevention and Treatment of Neurological and Psychiatric Diseases: Bibliometric Analysis of Patents","year":2022,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Psychiatry; China; Emerging technologies; Monopoly; Intervention (counseling); Business; Political science; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004654913,0.0006682892,0.001096174,0.09377049,0.0009683297,0.003695965,0.0006931868,0.0007029584,0.004188303],"category_scores_gemma":[0.02979183,0.0001549349,0.001874377,0.1043244,0.0006292082,0.003113322,0.001350177,0.0004175207,0.0005825048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001922411,"about_ca_system_score_gemma":0.003059617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005525999,"about_ca_topic_score_gemma":0.004136342,"domain_scores_codex":[0.9924501,0.001502866,0.001291041,0.0007879139,0.003579588,0.0003885587],"domain_scores_gemma":[0.9759941,0.01630296,0.004087968,0.000772996,0.002472293,0.0003696786],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002204683,0.0001917599,0.5421184,0.007886669,0.002317541,0.0006912654,0.001071845,0.007061508,0.001173672,0.01404157,0.01984913,0.4033762],"study_design_scores_gemma":[0.00006336752,0.0002147577,0.8621692,0.002139165,0.002988596,0.00170886,0.003432604,0.03357309,0.002039867,0.02300268,0.06852466,0.0001431126],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7393976,0.09737267,0.02462781,0.006212819,0.0004192737,0.0008522404,0.06068793,0.0006386816,0.06979109],"genre_scores_gemma":[0.9571841,0.0214272,0.006830063,0.0001067661,0.0002983788,0.0002568622,0.01262549,0.00003198603,0.001239162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9953451,"threshold_uncertainty_score":0.02461785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121612366342988,"score_gpt":0.3379483143715543,"score_spread":0.3167321907081244,"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."}}