{"id":"W4399848020","doi":"10.1007/s43441-024-00663-0","title":"Harmonizing Quality Improvement Metrics Across Global Trial Networks to Advance Paediatric Clinical Trials Delivery","year":2024,"lang":"en","type":"article","venue":"Therapeutic Innovation & Regulatory Science","topic":"Pharmaceutical studies and practices","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta","funders":"Canadian Institutes of Health Research; Food and Drug Administration; Innovative Medicines Initiative","keywords":"Metric (unit); Consistency (knowledge bases); Computer science; Identification (biology); Quality (philosophy); Interoperability; Clinical trial; Protocol (science); Data mining; Data science; Process management; Medicine; Operations management; Artificial intelligence; Engineering; World Wide Web","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0380117,0.0002404575,0.0006660023,0.000238319,0.0005024417,0.0003040634,0.0003411778,0.0001355062,0.00006075488],"category_scores_gemma":[0.004072685,0.0001838701,0.0001900531,0.01027821,0.0004896693,0.0006298738,0.0002378195,0.0004877532,0.00004799858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000516566,"about_ca_system_score_gemma":0.0006266587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001668493,"about_ca_topic_score_gemma":0.000002243365,"domain_scores_codex":[0.9949265,0.0004374764,0.001913883,0.0008046014,0.001241715,0.000675815],"domain_scores_gemma":[0.9961975,0.001780496,0.0004616807,0.000480551,0.0008188515,0.0002609155],"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":[0.006506446,0.0001665811,0.00216018,0.00005294375,0.0001420629,0.000007901274,0.0001130856,0.00008285489,0.00526163,0.0106209,0.0003807337,0.9745047],"study_design_scores_gemma":[0.05612938,0.006642362,0.2490522,0.0003777145,0.001320371,0.00005192172,0.001716136,0.05514728,0.007306906,0.006281619,0.6139302,0.002043983],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.951884,0.007172985,0.02871913,0.004672688,0.005242387,0.001606442,0.00002196821,0.0002064921,0.0004739406],"genre_scores_gemma":[0.9878608,0.0005092268,0.00103545,0.008874137,0.001523415,0.00004964512,0.000005229127,0.00001460798,0.0001274779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9724607,"threshold_uncertainty_score":0.9905694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3684120236295787,"score_gpt":0.5473139076095772,"score_spread":0.1789018839799985,"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."}}