{"id":"W3167722659","doi":"10.1038/s41394-020-00358-2","title":"The impact of data quality assurance and control solutions on the completeness, accuracy, and consistency of data in a national spinal cord injury registry of Iran (NSCIR-IR)","year":2021,"lang":"en","type":"article","venue":"Spinal Cord Series and Cases","topic":"Injury Epidemiology and Prevention","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Praxis Spinal Cord Institute","funders":"Tehran University of Medical Sciences and Health Services; Ministry of Health and Medical Education","keywords":"Medicine; Quality assurance; Data quality; Completeness (order theory); Consistency (knowledge bases); Missing data; Minimum Data Set; Data collection; Data set; Data mining; Data validation; Medical emergency; Database; Statistics; Operations management; Computer science; Artificial intelligence; Nursing; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2561938,0.000466885,0.0008585005,0.005952062,0.001552965,0.006052023,0.003058244,0.001161279,0.000717804],"category_scores_gemma":[0.4333287,0.0006578903,0.001611826,0.009237136,0.002865454,0.004085376,0.002790493,0.001985681,0.0001472212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006172589,"about_ca_system_score_gemma":0.01962569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04004188,"about_ca_topic_score_gemma":0.01809031,"domain_scores_codex":[0.7526446,0.1163002,0.05628068,0.01529476,0.05546367,0.004016038],"domain_scores_gemma":[0.366118,0.3330705,0.09985498,0.06840304,0.1293132,0.003240354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004805992,0.0002496814,0.8992707,0.0007557203,0.0006963409,0.00008882311,0.004426229,0.003777142,0.0007589654,0.004699504,0.003484782,0.08131152],"study_design_scores_gemma":[0.0001756163,0.0008345824,0.9328087,0.001744517,0.001558728,0.0004240485,0.005653239,0.0272514,0.00740436,0.004551651,0.01744589,0.0001471999],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936725,0.005549589,0.06516507,0.01457005,0.000734496,0.002148957,0.009391615,0.0006178218,0.008149999],"genre_scores_gemma":[0.9679545,0.0005543779,0.02605634,0.0008216892,0.0001291004,0.0004546436,0.003725705,0.00006724033,0.000236477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2561938,"threshold_uncertainty_score":0.9172454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2450615802151126,"score_gpt":0.4644030108445685,"score_spread":0.2193414306294559,"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."}}