{"id":"W4283374622","doi":"10.1111/hdi.13033","title":"Artificial intelligence and digital health for volume maintenance in hemodialysis patients","year":2022,"lang":"en","type":"review","venue":"Hemodialysis International","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Irish Research eLibrary; Health Research Board","keywords":"Medicine; Intensive care medicine; Hemodialysis; Volume overload; Intravascular volume status; Nephrology; Dialysis; Heart failure; Internal medicine; Hemodynamics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005587978,0.0005774353,0.0008895179,0.001817444,0.0001914718,0.001030534,0.0005153472,0.001084707,0.003414599],"category_scores_gemma":[0.001158355,0.0001649879,0.0007241789,0.001501202,0.0004497709,0.0011815,0.0006227107,0.001741322,0.001197679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000414261,"about_ca_system_score_gemma":0.0006575063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007676793,"about_ca_topic_score_gemma":0.001059341,"domain_scores_codex":[0.9997255,0.00007958253,0.00004146274,0.0000366187,0.00009997377,0.00001682852],"domain_scores_gemma":[0.9995607,0.0002993748,0.00004884839,0.00001289166,0.0000592377,0.0000190384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003930627,0.00005864799,0.00019179,0.01511162,0.0001639046,0.00007485464,0.00006296586,0.0003199988,0.0003940864,0.006144672,0.01803354,0.9594046],"study_design_scores_gemma":[0.00003898458,0.0001622251,0.002417286,0.01509485,0.0003279031,0.001316995,0.00009434337,0.0005487297,0.0005918574,0.008932525,0.9704306,0.00004357759],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000159838,0.9973947,0.0002615808,0.000437283,0.0002132599,0.000008170342,0.00001440987,0.000008046665,0.001502767],"genre_scores_gemma":[0.001745221,0.9966992,0.0004085138,0.0003733336,0.0002235031,0.00001262307,0.00002898201,0.000001963982,0.0005066453],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003414599,"threshold_uncertainty_score":0.01142299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05689540613684842,"score_gpt":0.3356413013941315,"score_spread":0.2787458952572831,"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."}}