{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.02137768,0.00008181554,0.0005165913,0.01798615,0.000008495332,0.00000166928,0.00009221596,0.000136121,0.0003662118],"category_scores_gemma":[0.04553096,0.00006333097,0.0001223712,0.04465939,0.0001199376,0.00005076361,0.00003247377,0.0001391191,0.000003645729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000408131,"about_ca_system_score_gemma":0.001988642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008276572,"about_ca_topic_score_gemma":0.0001174375,"domain_scores_codex":[0.9928178,0.001210731,0.0006226023,0.0002297418,0.004672575,0.0004465917],"domain_scores_gemma":[0.9955112,0.0006764299,0.0003762713,0.0003531707,0.002486812,0.0005961513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003457589,0.0003928671,0.1372625,0.000048971,0.0009599168,5.112111e-7,0.00004113185,0.00002880086,0.00010942,0.0001303134,0.0001126297,0.8605672],"study_design_scores_gemma":[0.004826023,0.0004274908,0.9554093,0.000009486316,0.003628753,0.000002598659,0.00004590081,0.02847496,0.003861516,0.003063481,0.0002069818,0.00004349291],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818812,0.003214289,0.0105402,0.000227801,0.00005038593,0.001821955,0.000008475128,0.000008279219,0.002247434],"genre_scores_gemma":[0.9974097,0.000475853,0.0007363918,0.0000166253,0.00003294864,0.001105717,0.0001813888,0.000006936452,0.00003441493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8605237,"threshold_uncertainty_score":0.9931442,"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."}}