{"id":"W4387635872","doi":"10.48550/arxiv.2310.08479","title":"Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut de Valorisation des Données; Université de Montréal","keywords":"Computer science; Parametric statistics; Machine learning; Artificial intelligence; Calibration; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006813577,0.0002795281,0.0002799031,0.0005109362,0.0002539116,0.0001543408,0.001038381,0.000392196,0.000009515359],"category_scores_gemma":[0.0001450721,0.0003766615,0.0001048182,0.000894143,0.00005668665,0.0006497472,0.0005921453,0.001439634,0.00007199836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005867562,"about_ca_system_score_gemma":0.0002437406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003893978,"about_ca_topic_score_gemma":0.002076964,"domain_scores_codex":[0.9971729,0.0005880116,0.0002868596,0.001423084,0.0001582363,0.0003709198],"domain_scores_gemma":[0.9983626,0.0001618876,0.0003003784,0.0008477746,0.0001793949,0.0001479371],"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":[0.00001692412,0.00004300016,0.3348575,0.0001429963,0.00001383534,0.00003604114,0.001566716,0.6535568,0.00007162018,0.007904097,0.000009130081,0.001781425],"study_design_scores_gemma":[0.0002728164,0.00007195306,0.09655575,0.00006918151,0.00001280319,0.000003532127,0.0003677605,0.8971866,0.000008238304,0.005107757,0.00005575198,0.0002878382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6056265,0.00001460969,0.3924193,0.0002646498,0.0003622079,0.0003952981,0.000004097496,0.000758493,0.0001548751],"genre_scores_gemma":[0.9977367,0.00005407748,0.0008985576,0.00003896963,0.00008383102,0.00001014973,0.0002030266,0.00003636389,0.0009383207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3921102,"threshold_uncertainty_score":0.9998685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06168694457300972,"score_gpt":0.2297027947797249,"score_spread":0.1680158502067152,"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."}}