{"id":"W4388153184","doi":"10.1016/j.heliyon.2023.e21586","title":"Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR)","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children","funders":"Garron Family Cancer Centre; Microsoft","keywords":"Computer science; Schema (genetic algorithms); Information retrieval; Database","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":[0.001853037,0.00009150882,0.0001928775,0.0001016387,0.0002848097,0.000004382485,0.0003080868,0.0001390486,0.00001569818],"category_scores_gemma":[0.0003450029,0.00006827059,0.00001314797,0.0003714159,0.00002446347,0.00009142051,0.0003803119,0.0004094031,0.00008151308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002203287,"about_ca_system_score_gemma":0.0009558681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002293985,"about_ca_topic_score_gemma":0.0008197445,"domain_scores_codex":[0.9977257,0.0006368554,0.0007169321,0.0002682821,0.0002600783,0.0003921792],"domain_scores_gemma":[0.9984672,0.0004762981,0.0002518111,0.0006931801,0.00005026471,0.00006121318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003174476,0.00003191311,0.9745004,0.003794593,0.00001457529,0.000005879865,0.01082218,0.000004419367,0.00314981,0.0001870818,0.004478207,0.002979213],"study_design_scores_gemma":[0.00115325,0.0000519838,0.7057523,0.005703082,0.00001411083,0.000005217492,0.004034094,0.0002840414,0.008287327,0.00006198198,0.2744337,0.000218902],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939669,0.0006872161,0.00003285301,0.001107143,0.0009086432,0.000792435,0.000009294621,0.00006429276,0.002431239],"genre_scores_gemma":[0.9948264,0.0003717392,0.0002445272,0.0004012767,0.0001454921,0.0001139566,0.00005766155,0.00002554596,0.003813387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2699555,"threshold_uncertainty_score":0.2783994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08295343321770635,"score_gpt":0.4082065225419245,"score_spread":0.3252530893242181,"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."}}