{"id":"W2805422302","doi":"10.1371/journal.pone.0198687","title":"Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Government of Ontario","keywords":"Logistic regression; Medicine; Intensive care unit; Quartile; Sentiment analysis; Intensive care; Artificial intelligence; Internal medicine; Computer science; Intensive care medicine","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":[0.001576969,0.0003097706,0.0003579937,0.001010887,0.0002045976,0.0007955575,0.0001557747,0.0002684357,0.0009210026],"category_scores_gemma":[0.009732574,0.00008665734,0.0004654102,0.0005505064,0.0001890673,0.0003413699,0.0004876249,0.0004142344,0.0002553115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003469544,"about_ca_system_score_gemma":0.0002256514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005959924,"about_ca_topic_score_gemma":0.001007521,"domain_scores_codex":[0.9992668,0.0002809604,0.0001201692,0.00009125486,0.000182514,0.00005831339],"domain_scores_gemma":[0.9941732,0.002543548,0.001809774,0.0001486927,0.001043353,0.0002814322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006025037,0.00007259467,0.9680809,0.0002039528,0.0001584532,0.0001136437,0.0003422132,0.0004512871,0.002611181,0.00005834442,0.001060745,0.02624413],"study_design_scores_gemma":[0.00001683465,0.0002645353,0.9881089,0.0001048461,0.0001875849,0.0003220311,0.0008356842,0.007332298,0.001511551,0.0002275236,0.001067332,0.00002084938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959857,0.0004729726,0.001204318,0.0002309666,0.0000607334,0.000039039,0.0008863693,0.00001833902,0.001101508],"genre_scores_gemma":[0.9975355,0.0002022537,0.001300959,0.00005561966,0.0000576107,0.00002364118,0.000681886,0.000003431394,0.000139203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001576969,"threshold_uncertainty_score":0.008339942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08978257306301946,"score_gpt":0.3596418232567152,"score_spread":0.2698592501936958,"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."}}