{"id":"W2765591468","doi":"10.1177/0840470417716470","title":"How health leaders can benefit from predictive analytics","year":2017,"lang":"en","type":"article","venue":"Healthcare Management Forum","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Predictive analytics; Analytics; Psychological intervention; Productivity; Health care; Risk analysis (engineering); Computer science; Data science; Business; Nursing; Medicine; Economics","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003092006,0.0006247322,0.001076897,0.0003850646,0.008362311,0.0003012878,0.001573046,0.0005133881,0.0001716502],"category_scores_gemma":[0.0007260563,0.0006301922,0.0002148648,0.0002841129,0.0003951594,0.0009935452,0.00128243,0.002164125,0.0003166911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002060509,"about_ca_system_score_gemma":0.001019784,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07608429,"about_ca_topic_score_gemma":0.1229605,"domain_scores_codex":[0.9910628,0.001819124,0.00148134,0.001411165,0.001310291,0.002915269],"domain_scores_gemma":[0.9913822,0.0006642803,0.002606187,0.003463445,0.0005319385,0.001351914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004232172,0.0002606402,0.6133466,0.0055457,0.000692223,0.0001411189,0.0117835,0.00001352662,0.000001724352,0.1024174,0.09867609,0.1666983],"study_design_scores_gemma":[0.002292264,0.0005814272,0.3948077,0.001627733,0.0001193184,0.000001668262,0.1374139,0.0005113038,0.000004450072,0.01412714,0.4477549,0.0007581715],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02556057,0.001650234,0.00229631,0.9469718,0.004357365,0.005120894,0.001164992,0.0005823665,0.01229544],"genre_scores_gemma":[0.8988228,0.002985431,0.001996924,0.0799247,0.001023876,0.0005871236,0.0004215263,0.000155889,0.01408169],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.8732623,"threshold_uncertainty_score":0.999615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5693339401366341,"score_gpt":0.5271846237332601,"score_spread":0.04214931640337394,"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."}}