{"id":"W3032680060","doi":"10.3760/cma.j.issn.0253-2352.2019.03.005","title":"Application of bibliometrics and visualization techniques to analyze the global research status and trends of rehabilitation after arthroplasty","year":2019,"lang":"en","type":"article","venue":"Zhonghua guke zazhi","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rehabilitation; Bibliometrics; Arthroplasty; Medicine; Science Citation Index; Specialty; Physical therapy; Orthopedic surgery; Bibliographic coupling; Citation; Family medicine; Library science; Surgery; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0143074,0.001540111,0.002262234,0.1713901,0.0009660766,0.005281592,0.0007834585,0.000520215,0.006166727],"category_scores_gemma":[0.04882001,0.0003649528,0.00282556,0.1732377,0.0005001268,0.003878017,0.00261898,0.0006886304,0.0008592951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002127523,"about_ca_system_score_gemma":0.004139604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007181045,"about_ca_topic_score_gemma":0.007069715,"domain_scores_codex":[0.9889824,0.002990185,0.002913095,0.0008726123,0.003871854,0.0003698913],"domain_scores_gemma":[0.9644614,0.0187774,0.008110846,0.001684902,0.006434693,0.0005306447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004248001,0.000204567,0.3600107,0.02387241,0.004859793,0.00074822,0.004991369,0.006175618,0.003146796,0.01350226,0.03363267,0.5484309],"study_design_scores_gemma":[0.0002143864,0.0004031677,0.7997501,0.00541196,0.00472821,0.001347025,0.009574347,0.03643278,0.003248669,0.02396924,0.1145761,0.0003439795],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5213937,0.08862751,0.09518362,0.00743122,0.00117232,0.004256199,0.2042346,0.01081575,0.06688503],"genre_scores_gemma":[0.8386629,0.02250873,0.09268491,0.0002024299,0.0006033588,0.003401705,0.03844716,0.0003741584,0.003114651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8286099,"threshold_uncertainty_score":0.07566565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009501825902898123,"score_gpt":0.327533344376754,"score_spread":0.3180315184738559,"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."}}