Insufficient Protein Intake Is Associated With Increased Mortality in 630 Patients With Cirrhosis Awaiting Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: For patients awaiting liver transplantation, we aimed to determine the prevalence and predictors of insufficient protein intake as well as to determine whether very low protein intake was an independent predictor of malnutrition and mortality. MATERIALS AND METHODS: Adults with cirrhosis who were activated on our local liver transplant waiting list between January 2000 and October 2009 were included. Estimated protein intake was derived from dietary records. Patients with incomplete dietary records were excluded. Multivariable logistic regression and competing risk analysis were used. RESULTS: Of 742 potential patients, 112 were excluded due to insufficient data, leaving 630 patients for evaluation. Mean protein intake was 1.0 ± 0.36 g/kg/d and only 24% of patients met the expert consensus recommended threshold of > 1.2 g/kg of protein per day. Very low protein intake (< 0.8 g/kg/d) was associated with worse liver disease severity (as measured by Child-Pugh or MELD). Protein intake below 0.8 g/kg/d was an independent predictor both of malnutrition as measured by the subjective global assessment (adjusted odds ratio [95% confidence interval (CI)]: 2.0 [1.3-3.0]) and of transplant waiting list mortality (adjusted hazard ratio [95% CI]: 1.8 [1.2-2.7]). CONCLUSION: In this large cohort of liver transplant waitlisted patients, very low protein intake was prevalent and independently associated with malnutrition and mortality. Unlike many other prognostic factors, protein intake is potentially modifiable. Prospective studies are warranted to evaluate the effect of targeted protein repletion on clinically relevant outcomes such as muscle mass, muscle function, immune function, and mortality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".