{"id":"W2419258133","doi":"","title":"[Application Status of Evaluation Methodology of Electronic Medical Record: Evaluation of Bibliometric Analysis].","year":2015,"lang":"en","type":"article","venue":"PubMed","topic":"Medical Research and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inclusion and exclusion criteria; Data extraction; Computer science; Inclusion (mineral); MEDLINE; China; Electronic medical record; Evaluation methods; Information retrieval; Database; Medical physics; Data science; Medicine; Psychology; Alternative medicine; Engineering; Political science; Pathology","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.1593641,0.001354618,0.003342434,0.06834493,0.001575231,0.008532487,0.002511032,0.001556621,0.003477527],"category_scores_gemma":[0.3176797,0.0007368009,0.002577489,0.1024534,0.002634617,0.007582199,0.003718492,0.0007702642,0.000874017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006855932,"about_ca_system_score_gemma":0.01717518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004489475,"about_ca_topic_score_gemma":0.004019318,"domain_scores_codex":[0.7874056,0.117736,0.03363835,0.003733232,0.05639023,0.001096662],"domain_scores_gemma":[0.7112613,0.1908962,0.03184665,0.009965093,0.05479988,0.001230887],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001452396,0.00009461385,0.01444823,0.06371462,0.001998555,0.00009857142,0.001773513,0.0007043699,0.0004092953,0.01650854,0.03398753,0.8661169],"study_design_scores_gemma":[0.0006743791,0.001430116,0.2444538,0.1615187,0.0122679,0.001827767,0.006381125,0.01846761,0.005877589,0.05795985,0.4883612,0.0007799119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02684624,0.7320681,0.123862,0.02502785,0.005590983,0.01339512,0.009215815,0.001891472,0.06210246],"genre_scores_gemma":[0.3441623,0.3283665,0.2908698,0.003742134,0.003376492,0.01776912,0.006754713,0.0005073188,0.004451578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.931655,"threshold_uncertainty_score":0.8428079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.350813896048509,"score_gpt":0.4981300265209667,"score_spread":0.1473161304724577,"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."}}