Prognostic Value of the ESPEN Consensus and Guidelines for Malnutrition: Prediction of Post‐Discharge Clinical Outcomes in Older Inpatients
Bibliographic record
Abstract
INTRODUCTION: Our study aimed to determine whether malnutrition and nutrition-related conditions using the European Society for Clinical Nutrition and Metabolism (ESPEN) consensus were associated with functional status, institutionalization, readmissions, and mortality in older patients at 3-month follow-up. METHODS: A cohort of 102 consecutive deconditioned patients was assessed at 3 months postdischarge from postacute geriatric care. Inclusion criteria were age ≥70 years, scores of Mini-Mental Status Examination ≥21/30, and being admitted for rehabilitation after an acute non-disabling disease. Malnutrition as defined by ESPEN consensus and nutrition-related conditions (such as frailty, sarcopenia, overweight/obesity, nutrient deficiency, and cachexia) was assessed, and related to postdischarge clinical outcomes at 3-month follow-up. RESULTS: Of 95 included patients (84.5 ± 6.5 years; 63.2% women), 31 had unintentional weight loss and 19 fulfilled malnutrition criteria defined by the ESPEN consensus. Nutrition-related conditions were frequent: 94 patients had frailty, 44 sarcopenia, 58 overweight/obesity, and 59 nutrient deficiency. Sarcopenia reduced functional status at 3-month follow-up (median difference: -25.5; 95% confidence interval (CI) -46.4 - -4.3, P = 0.008). Institutionalization was related to unintentional weight loss in univariate analysis (odds ratio (OR) = 3.9; 95%CI 1.3 - 12.4, P = 0.018). Meeting the basic ESPEN definition of malnutrition was related to institutionalization in univariate (OR = 3.4; 95%CI 1.0 to 11.3, P = 0.042) but not multivariate analysis, and was not significantly associated with readmissions or mortality at 3-month follow-up. CONCLUSIONS: Further research is needed on the potential value of the ESPEN consensus and guidelines for malnutrition to identify older patients at risk of worse functional status, institutionalization, readmissions, and mortality at 3-month follow-up postdischarge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".