{"id":"W4386871182","doi":"10.2196/44037","title":"Diabetes Life Expectancy Prediction Model Inputs and Results From Patient Surveys Compared With Electronic Health Record Abstraction: Survey Study","year":2023,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; University of Chicago Medicine","keywords":"Medicine; Life expectancy; Health and Retirement Study; Chart; Survey data collection; Gerontology; Expectancy theory; Demography; Psychology; Statistics; Environmental health; Population","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002514043,0.0002162796,0.0003236562,0.0002069639,0.0003854729,0.0001782711,0.0003121008,0.00004787231,0.000002325532],"category_scores_gemma":[0.000166885,0.0002028022,0.00002273046,0.0008189653,0.00002044103,0.0003976466,0.0001906832,0.000515748,0.00001102809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001914027,"about_ca_system_score_gemma":0.0003240328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004909553,"about_ca_topic_score_gemma":0.004256025,"domain_scores_codex":[0.9960347,0.001410793,0.0006207554,0.000833392,0.000485465,0.0006148635],"domain_scores_gemma":[0.9980602,0.0005693431,0.0003825784,0.00063181,0.0001296499,0.0002264798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000445855,0.0001182541,0.9485373,0.00003335048,0.00003673858,0.000005367757,0.01028371,0.02006948,0.000007334177,0.000008832456,0.0009773904,0.01987769],"study_design_scores_gemma":[0.0005035449,0.0003926687,0.5970578,0.00005171095,0.000001495422,5.442795e-7,0.000157763,0.401631,0.000003454458,0.00005391166,0.00003142547,0.0001146794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819554,0.0001218005,0.01451656,0.001501067,0.0002720098,0.0007877164,0.00006850819,0.0007391809,0.00003777642],"genre_scores_gemma":[0.9983312,0.00002522343,0.001040891,0.0001798053,0.00007582115,0.0001193893,0.0001743451,0.00002574446,0.00002758543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3815615,"threshold_uncertainty_score":0.8270034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03728406813547757,"score_gpt":0.3078940830435898,"score_spread":0.2706100149081123,"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."}}