{"id":"W4317749998","doi":"10.1093/jamia/ocad002","title":"MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children","funders":"Hospital for Sick Children","keywords":"Interoperability; Data format; Computer science; Patient data; World Wide Web; Database; Computer hardware","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":[0.002186839,0.00009018305,0.0004148388,0.0003320689,0.00006431572,0.000008197346,0.0004653677,0.0001195098,0.000007857411],"category_scores_gemma":[0.004502903,0.00005867532,0.00006312576,0.0007441965,0.00007402491,0.0003267096,0.0002082004,0.0007883853,0.00000969177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004301028,"about_ca_system_score_gemma":0.0002614593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001493624,"about_ca_topic_score_gemma":0.00004906179,"domain_scores_codex":[0.9973794,0.0001383686,0.001178323,0.00005623133,0.001014605,0.0002331128],"domain_scores_gemma":[0.9970345,0.0003116595,0.00194568,0.0003433447,0.0002163082,0.0001484602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005845766,0.0004670575,0.5813503,0.0005722138,0.0003189739,0.00004767495,0.03611781,0.0002874057,0.0005648521,0.00007998382,0.0113601,0.3682491],"study_design_scores_gemma":[0.009564919,0.01488618,0.1912773,0.01073807,0.0004375151,0.0003727046,0.1155,0.6221735,0.01377399,0.00122579,0.01924781,0.0008021505],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995332,0.00002078803,0.0001037474,0.003755893,0.0004707327,0.000160717,0.000003799816,0.00002213459,0.000130205],"genre_scores_gemma":[0.9970573,0.0002694687,0.0007353642,0.001799355,0.00009951002,0.000002763067,0.00001429304,0.000008403709,0.00001351924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6218862,"threshold_uncertainty_score":0.5390721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04824375118723655,"score_gpt":0.3648114203584823,"score_spread":0.3165676691712457,"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."}}