{"id":"W2583036857","doi":"10.9778/cmajo.20160108","title":"Estimating patient-borne water and electricity costs in home hemodialysis: a simulation","year":2017,"lang":"en","type":"article","venue":"CMAJ Open","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nickel Institute; University of Alberta; Alberta Health Services","funders":"","keywords":"Home hemodialysis; Hemodialysis; Dialysis; Medicine; Staffing; Medical prescription; Overhead (engineering); Electricity; Total cost; Consumption (sociology); Operations management; Emergency medicine; Computer science; Business; Surgery; Economics; Engineering; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001966927,0.0006396759,0.0007266887,0.00080423,0.000573012,0.0009441993,0.001273864,0.001448793,0.002093993],"category_scores_gemma":[0.006561918,0.0005501811,0.00115269,0.001001078,0.0005602076,0.0009470211,0.0008019028,0.0009172929,0.0001010829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003070908,"about_ca_system_score_gemma":0.002285904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05057883,"about_ca_topic_score_gemma":0.02691645,"domain_scores_codex":[0.9992879,0.0004215886,0.00003707095,0.00008660553,0.00007185117,0.00009507629],"domain_scores_gemma":[0.9918461,0.00691489,0.0004542442,0.0002053824,0.000370079,0.0002092677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001197209,0.0001280487,0.007181734,0.00002111175,0.00003725179,0.00006989268,0.00002912135,0.9895574,0.00009689415,0.0007332686,0.0001481045,0.001877371],"study_design_scores_gemma":[0.00007830156,0.00009595511,0.00206857,0.000008618369,0.00002684628,0.00002828896,0.00005253465,0.9965373,0.0001784105,0.0007355969,0.0001776149,0.00001194971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794124,0.0001096191,0.01590676,0.0002924515,0.00001793488,0.0001472232,0.0005645152,0.00006329425,0.003485734],"genre_scores_gemma":[0.9877481,0.00007954001,0.01119458,0.00004118897,0.000006517315,0.0001148831,0.0002911576,0.00001130339,0.0005128048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05057883,"threshold_uncertainty_score":0.1005689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200717782148999,"score_gpt":0.309996521917762,"score_spread":0.287989344096272,"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."}}