Relationships Between Nutrition Risk and Frailty in Candidates for Lung Transplant
Bibliographic record
Abstract
BACKGROUND: Healthcare teams evaluating candidates for lung transplant seek to identify and address modifiable factors to improve their clinical outcomes. Frailty may be a modifiable factor, and poor nutrition may be a contributor to frailty. This study evaluated the relationship between nutrition risk and frailty in lung transplant candidates. METHODS: This was a secondary analysis of data from 62 adult lung transplant candidates. Nutrition risk was assessed with the Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN-14) questionnaire, and frailty was measured using two methods: physical frailty with the Fried frailty index (FFI) and multidimensional frailty using a cumulative deficits frailty index (CDFI), where higher scores on a 0-1 scale denote increasing frailty. Pearson correlation, independent-samples t-test, and Fisher's exact tests analyzed associations between SCREEN-14 and both FFI and CDFI scores. RESULTS: Most participants were at high nutrition risk (83.9%) and were pre-frail (69.4%) or frail (17.7%) when assessed using FFI. Mean CDFI score was 0.26. Higher nutrition risk was associated with a higher degree of frailty as measured using FFI (r = -0.303; p = 0.017) but not CDFI. Participants at high nutrition risk were significantly more likely to be pre-frail or frail by FFI than those at low nutrition risk (92.3% vs. 60.0%, respectively; p = 0.019). CONCLUSIONS: High nutrition risk is highly prevalent and associated with physical frailty in lung transplant candidates. Future studies should investigate how to best identify nutrition risk and whether interventions that reduce this risk also decrease frailty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".