{"id":"W4318931844","doi":"10.1161/circoutcomes.122.009277","title":"Methods to Enhance Causal Inference for Assessing Impact of Clinical Informatics Platform Implementation","year":2023,"lang":"en","type":"article","venue":"Circulation Cardiovascular Quality and Outcomes","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Informatics; Health informatics; Causal inference; Intensive care unit; Emergency medicine; Medical emergency; Intensive care medicine; Nursing; Public health","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":[],"consensus_categories":[],"category_scores_codex":[0.02511581,0.0001772658,0.001152165,0.0002064577,0.00043558,0.00002322759,0.0001133309,0.0002559306,0.00002549117],"category_scores_gemma":[0.003778065,0.0001509378,0.0007849599,0.0004929226,0.00003521279,0.0003918847,0.00008060195,0.0003158335,0.00002379999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002671705,"about_ca_system_score_gemma":0.0009474336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001243024,"about_ca_topic_score_gemma":0.0001252144,"domain_scores_codex":[0.9942126,0.001896857,0.002648676,0.0002583853,0.0004313626,0.0005521488],"domain_scores_gemma":[0.9927431,0.005370425,0.0007298302,0.0005134899,0.0004289017,0.0002142056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001849015,0.000008970579,0.7857798,0.001110125,0.0005992173,1.491641e-7,0.003372155,0.0009708617,0.00005050818,0.00108173,0.0001353669,0.2068726],"study_design_scores_gemma":[0.0008873637,0.00005791882,0.9894937,0.00008313445,0.00009036162,5.479466e-7,0.003168833,0.00248834,0.00003234785,0.0008117242,0.00271734,0.0001684154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6516941,0.0001005933,0.3459029,0.0001343724,0.0005051763,0.001446125,0.00003922926,0.00007859778,0.00009891064],"genre_scores_gemma":[0.9864954,0.00006363076,0.01240246,0.0002843176,0.0001945434,0.000382541,0.0001227317,0.00002263044,0.00003175023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3348013,"threshold_uncertainty_score":0.870469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4107656592187916,"score_gpt":0.6884051209880567,"score_spread":0.2776394617692651,"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."}}