{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03100806,0.000776256,0.0008448666,0.0008760652,0.0002447538,0.001445862,0.0006872261,0.0006501947,0.001453278],"category_scores_gemma":[0.08144827,0.0003922812,0.002206392,0.001115531,0.000376609,0.001345734,0.001435085,0.0009686318,0.0003220976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007831667,"about_ca_system_score_gemma":0.0008374538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00229276,"about_ca_topic_score_gemma":0.001800854,"domain_scores_codex":[0.9735907,0.02036898,0.001635356,0.001763007,0.002092947,0.0005490936],"domain_scores_gemma":[0.9160675,0.06203767,0.01017186,0.004618764,0.005513606,0.001590562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003598904,0.0008004698,0.9835381,0.0001016622,0.001148126,0.00002284088,0.0003335092,0.001542933,0.0001183266,0.00004206874,0.0002721926,0.008480798],"study_design_scores_gemma":[0.0004924738,0.009626962,0.9631267,0.0000991335,0.001021132,0.0001158624,0.0006435949,0.02342947,0.0007373129,0.0001293386,0.0005322662,0.00004572118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975412,0.00009878421,0.001227887,0.00004379699,0.00001125719,0.0001137347,0.000607681,0.00001968794,0.0003358905],"genre_scores_gemma":[0.9984195,0.00004281773,0.0006241924,0.00003504019,0.000009694104,0.0001222815,0.0006747643,0.000005743299,0.00006589696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03100806,"threshold_uncertainty_score":0.1639883,"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."}}