{"id":"W6976991043","doi":"10.6084/m9.figshare.18132463.v2","title":"Additional file 1 of Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Outcome (game theory); Mixed model; Critically ill; Additive model; Log-linear model; Exponential function; Range (aeronautics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002004532,0.001122153,0.001534313,0.002441507,0.0006495902,0.001702367,0.001903777,0.001213382,0.7547046],"category_scores_gemma":[0.04319666,0.0006262345,0.001536042,0.00468931,0.0002719627,0.001498423,0.0009314025,0.001122003,0.07161187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008279024,"about_ca_system_score_gemma":0.001559146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008841524,"about_ca_topic_score_gemma":0.009360342,"domain_scores_codex":[0.9989581,0.0002683058,0.0002166583,0.0002894564,0.0001478427,0.0001197678],"domain_scores_gemma":[0.9650298,0.0291804,0.001917869,0.001547528,0.001777003,0.0005474877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00114681,0.0002094627,0.01762969,0.004763066,0.0004334433,0.0001633885,0.0001125307,0.00135856,0.000130684,0.001113288,0.9565251,0.01641391],"study_design_scores_gemma":[0.02327329,0.001499881,0.2220901,0.0104989,0.002452509,0.002598684,0.001396565,0.01410665,0.001660503,0.02453909,0.6953675,0.0005162751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004872395,0.00002394416,0.0002677141,0.00007159594,0.00001247166,0.00005524665,0.9986691,0.0001167673,0.0002959331],"genre_scores_gemma":[0.02525914,0.0002209742,0.005027144,0.0005168904,0.0001563494,0.002648521,0.9593353,0.0005808339,0.006254968],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7547046,"threshold_uncertainty_score":0.3498841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08056523391962636,"score_gpt":0.3402434651559146,"score_spread":0.2596782312362883,"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."}}