Evaluation of Malnutrition Risk in Lung Transplant Candidates Using the Nutritional Risk Index
Bibliographic record
Abstract
Background. Malnutrition in lung transplant (LTx) candidates is an important risk factor for adverse outcomes. We sought to evaluate the Nutritional Risk Index (NRI) in LTx candidates, a validated measure of malnutrition risk in chronic disease. We aimed to characterize malnutrition risk using NRI, evaluate change in body weight between nutritional risk groups, and assess association of malnutrition risk with pretransplant and posttransplant outcomes. Methods. Retrospective, single-center cohort study of LTx candidates (2014–2015) evaluated by a dietitian before listing. Nutritional parameters, weight change pretransplant and posttransplant, and clinical outcomes were abstracted up to 1-year posttransplant. NRI was calculated as follows: (1.519 × albumin) + (41.7 × current weight/ideal weight) with high malnutrition risk defined as the lowest quartile of NRI for cystic fibrosis (CF) and non-CF patients. Results. The cohort comprises 247 LTx candidates (57% male; median age 59 y; non-CF 88%). Non-CF candidates had a greater mean NRI compared with CF patients (109 ± 11 versus 95 ± 12; P < 0.0001). 86% with high malnutrition risk maintained/gained weight (≥5%) pretransplant. In 196 LTx recipients, malnutrition risk was not associated with hospital stay, discharge disposition, or 1-year mortality. The median percent weight gain for LTx recipients in the first year was 10.5% (4.0–20.1), with high malnutrition risk recipients having comparable or greater weight gain to the low-risk group (mean difference for non CF: 6.8%; P = 0.02 and CF: −3.8%; P = 0.65). Conclusions. Malnutrition risk assessed with NRI was not prognostic of posttransplant outcomes in this retrospective cohort. LTx candidates with high malnutrition risk were able to maintain their weight pretransplant and demonstrated considerable weight gain in the first-year posttransplant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".