{"id":"W3201807216","doi":"10.3389/fped.2021.711200","title":"Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System","year":2021,"lang":"en","type":"article","venue":"Frontiers in Pediatrics","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Mount Sinai Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Audit; Data collection; Data extraction; Data quality; Quality assurance; Quality management; Data element; Database; Service (business); MEDLINE; Statistics; Computer science; Accounting; Metadata","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009346218,0.0001197859,0.0003299779,0.00004027992,0.00007750872,0.00002120955,0.001035627,0.000074609,0.000006166902],"category_scores_gemma":[0.0003581202,0.00008316959,0.0000190401,0.0005370404,0.00004435565,0.0001853567,0.003448748,0.0002637632,8.247474e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001120654,"about_ca_system_score_gemma":0.0003244244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009016345,"about_ca_topic_score_gemma":0.0004805621,"domain_scores_codex":[0.9983057,0.0001088978,0.0004836859,0.0005117508,0.0003724047,0.0002175934],"domain_scores_gemma":[0.9970948,0.0000374503,0.0002216969,0.002486623,0.00006732382,0.00009210278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001104976,0.00005411733,0.8052204,0.0007636042,0.00003678757,0.00002428877,0.00004306595,0.000006411934,0.000004485653,0.00003030387,0.1895587,0.004147353],"study_design_scores_gemma":[0.002231768,0.00009012532,0.9502783,0.0002333264,0.0002684317,0.0001253799,0.0009035855,0.02266273,0.000007417292,0.000245992,0.02277935,0.0001735373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9489208,0.01605229,0.01279385,0.002713152,0.01079153,0.0007965619,0.007163626,0.00004524151,0.0007229641],"genre_scores_gemma":[0.9739311,0.003948414,0.01392023,0.0006680807,0.002770946,0.00000384668,0.004038163,0.00002513949,0.0006941294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1667793,"threshold_uncertainty_score":0.4298618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211720390799048,"score_gpt":0.3015902167681557,"score_spread":0.2794730128601652,"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."}}