{"id":"W2613165132","doi":"10.1055/s-0037-1603325","title":"Internal Audit of the Canadian Neonatal Network Data Collection System","year":2017,"lang":"en","type":"article","venue":"American Journal of Perinatology","topic":"Neonatal Respiratory Health Research","field":"Medicine","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital; Hamilton Health Sciences; McMaster Children's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Audit; Benchmarking; Data extraction; Data collection; Reliability (semiconductor); Data quality; Database; MEDLINE; Statistics; Accounting; Computer science; Operations management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009282387,0.00009652975,0.0004999279,0.0001619006,0.0004733883,0.00002403857,0.001266853,0.0000600124,0.00005603118],"category_scores_gemma":[0.0009440397,0.00006667966,0.0000886085,0.0001787735,0.00100261,0.0001160816,0.0003011014,0.0006429872,0.000007305857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004836084,"about_ca_system_score_gemma":0.003503816,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1005549,"about_ca_topic_score_gemma":0.1135914,"domain_scores_codex":[0.9983699,0.0002152673,0.0004663924,0.0001580967,0.0004183878,0.0003719723],"domain_scores_gemma":[0.9970382,0.000116535,0.001004186,0.001018851,0.0004189631,0.0004032156],"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":[0.002093446,0.00005006334,0.2283085,0.0004585445,0.0004040602,0.002171803,0.0003859493,0.00001996547,0.00007962581,0.002029566,0.007114497,0.756884],"study_design_scores_gemma":[0.001417192,0.001707199,0.2362801,0.0005406348,0.00008245055,0.01073722,0.0008527049,0.0008390984,0.0002448984,0.00003343757,0.7471544,0.0001106451],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871677,0.001059252,0.0001332087,0.007425949,0.001504638,0.0002278004,0.0000232497,0.000005315369,0.002452889],"genre_scores_gemma":[0.9987259,0.0000539605,0.0004433723,0.0002694294,0.0003136006,0.000001879862,0.000001103138,0.00001455903,0.0001761933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7567733,"threshold_uncertainty_score":0.9054346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04851964728599771,"score_gpt":0.3762777488635038,"score_spread":0.3277581015775061,"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."}}