Systematic review with meta‐analysis: Nutritional screening and assessment tools in cirrhosis
Bibliographic record
Abstract
BACKGROUND & AIMS: Disease-related malnutrition is common in cirrhosis. Multiple studies have evaluated nutritional screening tools (NSTs, rapid bedside tests targeting who needs assessment) and nutritional assessment tools (NATs, used in diagnosing malnutrition) as predictors of clinical outcome in this population. We performed a systematic review and meta-analysis of this literature with the aim of summarising the varying definitions of malnutrition across studies, the available evidence for NSTs and the ability of NSTs and NATs to predict clinical outcomes in cirrhosis. METHODS: The primary outcome measures were pre- and post-transplant mortality with a range of secondary outcomes. Inclusion: cirrhosis over age 16. Exclusion: >25% with hepatocellular carcinoma, primarily laboratory test-based NATs or lack of screening, assessment or outcome criteria. RESULTS: Eight thousand eight hundred fifty patients were included across 47 studies. Only 3 studies assessed NSTs. Thirty-two definitions for malnutrition were utilised across studies. NATs predicted pre-transplant mortality in 69% of cases that were assessed with a risk ratio (RR) of 2.38 (95% CI 1.96-2.89). NATs were prognostic for post-transplant mortality only 28% of the times they were assessed, with a RR of 3.04 (95% CI 1.51-6.12). CONCLUSIONS: The cirrhosis literature includes limited data on nutrition screening and multiple definitions for what constitutes malnutrition using NATs. Despite this discordance, it is clear that malnutrition is a valuable predictor of pre-transplant mortality almost regardless of how it is defined. We require clinical and research consensus around the definition of malnutrition and the accepted processes and cut-points for nutrition screening and assessment in cirrhosis.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.047 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".