{"id":"W2912603409","doi":"10.1097/cin.0000000000000508","title":"Nurses “Seeing Forest for the Trees” in the Age of Machine Learning","year":2019,"lang":"en","type":"article","venue":"CIN Computers Informatics Nursing","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Providence Health Care","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Context (archaeology); Relevance (law); Data pre-processing; Decision tree; Knowledge management; Nursing; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01314216,0.0008049825,0.0006290193,0.001599014,0.005965478,0.00703634,0.001467868,0.007235698,0.008177824],"category_scores_gemma":[0.05302037,0.0007143752,0.0008286708,0.001162815,0.01233932,0.02064612,0.005265109,0.01419965,0.004514067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002096866,"about_ca_system_score_gemma":0.004710887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005915514,"about_ca_topic_score_gemma":0.0104802,"domain_scores_codex":[0.9908043,0.006284489,0.0002844068,0.0006388845,0.001361551,0.0006263317],"domain_scores_gemma":[0.9755068,0.01504887,0.001531686,0.001758275,0.003601645,0.0025528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000273673,0.0002361707,0.008630883,0.0005186275,0.00008330981,0.001240653,0.02301134,0.002173454,0.0008552185,0.1262718,0.5086201,0.3280847],"study_design_scores_gemma":[0.00006624592,0.0002247622,0.001586496,0.00139751,0.00003049852,0.001657078,0.01681422,0.004562199,0.0006626955,0.3945681,0.5782781,0.0001520851],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01303289,0.01450051,0.05517596,0.884713,0.006669609,0.000074723,0.0001157341,0.0006286085,0.02508902],"genre_scores_gemma":[0.3387891,0.03076254,0.1490903,0.4320095,0.009827324,0.0004421693,0.0002598575,0.001077797,0.03774145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01314216,"threshold_uncertainty_score":0.06950319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870131187164699,"score_gpt":0.2938190027896754,"score_spread":0.2751176909180284,"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."}}