{"id":"W2791417851","doi":"10.1007/s12603-018-1016-6","title":"Modified Texture Food Use is Associated with Malnutrition in Long Term Care: An Analysis of Making the Most of Mealtimes (M3) Project","year":2018,"lang":"en","type":"article","venue":"The journal of nutrition health & aging","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network; University of Alberta; University of Manitoba; Université de Moncton; University of Guelph; Research Institute for Aging; Toronto Rehabilitation Institute; University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"Malnutrition; Term (time); Texture (cosmology); Food science; Environmental health; Business; Medicine; Computer science; Artificial intelligence; Chemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002212039,0.0001848395,0.0008568917,0.001167447,0.0002980347,0.0000286668,0.000205417,0.0001038923,0.00002194777],"category_scores_gemma":[0.0001291108,0.0001152598,0.0001590477,0.002139748,0.0001950684,0.0003168317,0.00002421279,0.0006194486,2.144954e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004277301,"about_ca_system_score_gemma":0.0004443807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000371948,"about_ca_topic_score_gemma":0.0008694053,"domain_scores_codex":[0.996682,0.0007249052,0.001322836,0.0001745615,0.0007082027,0.0003875239],"domain_scores_gemma":[0.9961834,0.000378178,0.001768066,0.0003362124,0.00120624,0.0001279157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01531982,0.009980564,0.7620817,0.01761989,0.004492817,0.0001408099,0.1584701,0.0006632235,0.004710712,0.0007245786,0.005401308,0.02039446],"study_design_scores_gemma":[0.0133771,0.00730918,0.9303171,0.02177519,0.002299349,0.0003979849,0.01534683,0.006300437,0.002052171,0.0002974711,0.000217681,0.0003095343],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838445,0.003524436,0.0009985056,0.01038609,0.00008327675,0.001035833,0.00007911027,0.00001925181,0.00002903183],"genre_scores_gemma":[0.9944215,0.001461278,0.000976248,0.002798709,0.0002446009,0.00001159731,0.00005514094,0.00002501466,0.000005886567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1682353,"threshold_uncertainty_score":0.4700159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07502130071963227,"score_gpt":0.3888352526576404,"score_spread":0.3138139519380081,"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."}}