{"id":"W2095906312","doi":"10.1093/ckj/sfv003","title":"Optimization of the convection volume in online post-dilution haemodiafiltration: practical and technical issues","year":2015,"lang":"en","type":"article","venue":"Clinical Kidney Journal","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Saint-Luc; Centre Hospitalier de l’Université de Montréal","funders":"Kidney Research UK","keywords":"Volume (thermodynamics); Dilution; Convection; Filtration (mathematics); Volume fraction; Mechanics; Medicine; Computer science; Thermodynamics; Mathematics; Statistics; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.001449434,0.00007956311,0.0002719113,0.00007975125,0.00004457463,0.0000314701,0.0000560521,0.0001183852,0.00006909786],"category_scores_gemma":[0.007129694,0.00004894362,0.0001384987,0.0002002408,0.0001907183,0.0001414119,0.00005635366,0.0004271513,0.000004147822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005606555,"about_ca_system_score_gemma":0.0005063583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001151454,"about_ca_topic_score_gemma":0.000007273823,"domain_scores_codex":[0.9983892,0.0002896848,0.0007201165,0.0001468925,0.0003460295,0.0001080041],"domain_scores_gemma":[0.9987386,0.00007279403,0.0002514196,0.0001540666,0.0003448338,0.0004382847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005132529,0.01208712,0.669352,0.0002692187,0.0005734944,0.000265794,0.0004465849,0.007696198,0.001034014,0.006002123,0.2715568,0.02558414],"study_design_scores_gemma":[0.006926081,0.001837721,0.9102678,0.0005660495,0.0007537511,0.0008496629,0.000542418,0.05793395,0.00006243384,0.001397327,0.01866017,0.0002026151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5784435,0.0007570768,0.03476935,0.3787774,0.002336461,0.001193441,0.00003500613,0.00006092882,0.003626789],"genre_scores_gemma":[0.9785341,0.0006147099,0.01607007,0.003357138,0.0008704246,0.000003880998,0.00005069295,0.00001302176,0.000485973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4000906,"threshold_uncertainty_score":0.8535425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06726766814006646,"score_gpt":0.3970663550261632,"score_spread":0.3297986868860968,"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."}}