{"id":"W4284676289","doi":"10.1007/s00247-022-05427-2","title":"Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence","year":2022,"lang":"en","type":"review","venue":"Pediatric Radiology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Stewardship (theology); Corporate governance; Data governance; Context (archaeology); Information governance; Field (mathematics); Knowledge management; Computer science; Medicine; Data science; Artificial intelligence; Process management; Information system; Business; Data quality; Political science; Management information systems","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01288121,0.000632215,0.002971705,0.002510939,0.0006019818,0.00004250147,0.003068781,0.0007902809,0.0008183863],"category_scores_gemma":[0.0225655,0.0005971771,0.0001895962,0.006470334,0.0003264555,0.0003067044,0.003196518,0.003286428,0.0009103919],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001891838,"about_ca_system_score_gemma":0.01191313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001039675,"about_ca_topic_score_gemma":0.001185468,"domain_scores_codex":[0.988018,0.003597924,0.00308777,0.002798787,0.000853213,0.001644261],"domain_scores_gemma":[0.9831425,0.009936024,0.0008182247,0.005208694,0.0004113152,0.0004832647],"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":[0.0003657949,0.000432204,0.003541789,0.009271746,0.0001611597,0.0004625567,0.0006899148,0.00009707015,2.865118e-7,0.0005756178,0.07774996,0.9066519],"study_design_scores_gemma":[0.0000387594,0.001365935,0.0001411576,0.000304583,0.00170805,0.001143472,0.001753775,0.0002776627,3.613569e-7,0.001305001,0.9913247,0.0006365668],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001469688,0.9859732,0.0003277403,0.00145418,0.006174855,0.002735414,0.001639497,0.00007730326,0.0001480733],"genre_scores_gemma":[0.0005209098,0.9829696,0.00132894,0.0001657601,0.009599818,0.0004210063,0.004551829,0.0001122996,0.0003298424],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9135747,"threshold_uncertainty_score":0.9998675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8647738403172318,"score_gpt":0.62274225830464,"score_spread":0.2420315820125918,"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."}}