{"id":"W2075357812","doi":"10.2196/medinform.2984","title":"A Validation of an Intelligent Decision-Making Support System for the Nutrition Diagnosis of Bariatric Surgery Patients","year":2014,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pontificia Universidade Católica do Paraná","keywords":"Gold standard (test); Medical diagnosis; Medicine; Malnutrition; Surgery; Receiver operating characteristic; Anemia; Expert system; Radiology; Artificial intelligence; Internal medicine; Computer science","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":[0.00482883,0.0006042551,0.0005738455,0.001135216,0.000398446,0.0008870423,0.0009771888,0.0008673167,0.002167072],"category_scores_gemma":[0.01414326,0.0001786533,0.0003478167,0.0004567267,0.0002985832,0.0005602014,0.0005477264,0.0003285132,0.0007182868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006482617,"about_ca_system_score_gemma":0.001131961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001710418,"about_ca_topic_score_gemma":0.0007326234,"domain_scores_codex":[0.9977896,0.0009523966,0.0002878525,0.0004702932,0.0004172024,0.00008251841],"domain_scores_gemma":[0.992465,0.004286058,0.0004205017,0.0004939816,0.00204164,0.0002928151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.009468059,0.003521268,0.2118046,0.0009677735,0.0005159647,0.001766727,0.00133559,0.05328088,0.06102653,0.001485101,0.009447769,0.6453797],"study_design_scores_gemma":[0.001184334,0.003527079,0.07003114,0.0003741738,0.0004304768,0.001433138,0.0005339013,0.8540663,0.05965367,0.001357615,0.007292957,0.0001151423],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8660394,0.0003636081,0.1225754,0.0005344009,0.000226081,0.001053732,0.001172918,0.004919528,0.003115089],"genre_scores_gemma":[0.8979477,0.00006165449,0.09955226,0.0001714462,0.00003174337,0.000389297,0.001197028,0.00003210561,0.0006168367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00482883,"threshold_uncertainty_score":0.02553755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0341248568986311,"score_gpt":0.3545044140779079,"score_spread":0.3203795571792768,"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."}}