{"id":"W2097302145","doi":"10.4338/aci-2013-04-ra-0029","title":"Comparing predictions made by a prediction model, clinical score, and physicians","year":2013,"lang":"en","type":"article","venue":"Applied Clinical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"McNemar's test; Predictive modelling; Receiver operating characteristic; Naive Bayes classifier; Machine learning; Artificial intelligence; Emergency department; Computer science; Medicine; Data mining; Statistics; Support vector machine; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01882009,0.001475011,0.0009386698,0.001874326,0.0003284934,0.001308634,0.0006713818,0.001289467,0.001070496],"category_scores_gemma":[0.04951911,0.0004007797,0.00122852,0.0008310668,0.0005060592,0.001249168,0.0008342501,0.001178957,0.0004488775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009138579,"about_ca_system_score_gemma":0.001038442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00368128,"about_ca_topic_score_gemma":0.001729531,"domain_scores_codex":[0.9909203,0.005808856,0.0005659919,0.001191844,0.001152116,0.0003609686],"domain_scores_gemma":[0.9204109,0.07076976,0.002430727,0.001870506,0.003472141,0.001045991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005107482,0.001299868,0.8147806,0.0002129922,0.001513675,0.000193844,0.0003222004,0.135433,0.001169674,0.0002887692,0.002011053,0.03766682],"study_design_scores_gemma":[0.0005076114,0.006988183,0.2444846,0.0001691584,0.0008683582,0.0004163973,0.0003756747,0.7395808,0.004540249,0.001044884,0.0009232903,0.0001008959],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982955,0.0005094081,0.01392063,0.0003600804,0.0000773009,0.0001648109,0.000809415,0.0001501452,0.00105316],"genre_scores_gemma":[0.9940324,0.00008933488,0.004442572,0.00004722547,0.00002648641,0.00007700811,0.001146307,0.00001194075,0.0001266218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01882009,"threshold_uncertainty_score":0.09953135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06969168858235335,"score_gpt":0.3510963986171176,"score_spread":0.2814047100347642,"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."}}