{"id":"W4397048097","doi":"10.1681/asn.20233411s1175c","title":"Characteristics of Global Dialysis Data from Multiple Providers in the New MONitoring Dialysis Outcomes (MONDO) Dataset","year":2023,"lang":"en","type":"article","venue":"Journal of the American Society of Nephrology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Dialysis; Medicine; Intensive care medicine; Nephrology; Home dialysis; Internal 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":[0.004402397,0.0004827887,0.0006013617,0.002277424,0.0005249725,0.001120961,0.000886569,0.0008091431,0.002263204],"category_scores_gemma":[0.01708443,0.0002091759,0.0006861822,0.005382832,0.0004790415,0.0008488889,0.001626995,0.0008857218,0.001053808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118025,"about_ca_system_score_gemma":0.001728339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008420539,"about_ca_topic_score_gemma":0.007631163,"domain_scores_codex":[0.9962978,0.000832786,0.0007140043,0.0007716403,0.0009643411,0.0004193207],"domain_scores_gemma":[0.9874324,0.004089861,0.003528967,0.002001501,0.002153229,0.000794136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001207634,0.000260124,0.7581307,0.0006801742,0.0004238586,0.0005474701,0.0007186764,0.006844255,0.0009991351,0.002212112,0.2050045,0.02297131],"study_design_scores_gemma":[0.0003727941,0.0002417719,0.8248469,0.0004486024,0.000163516,0.001673911,0.001248053,0.00805677,0.001732421,0.002562846,0.1585359,0.0001164062],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1988404,0.0005753408,0.001699261,0.001172092,0.00007030315,0.0002079288,0.7950468,0.0002765026,0.002111317],"genre_scores_gemma":[0.1581923,0.0001904058,0.002658308,0.0003664844,0.00004703319,0.0005020516,0.8374777,0.00006100622,0.000504643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008420539,"threshold_uncertainty_score":0.02328241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1841195792030918,"score_gpt":0.4720053762408953,"score_spread":0.2878857970378035,"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."}}