Nutritional Requirements of Lung Transplant Recipients: Challenges and Considerations
Bibliographic record
Abstract
An optimal nutritional status is associated with better post-transplant outcomes and survival. Post-lung transplant nutrition management is however particularly challenging as lung recipients represent a very heterogeneous group of patients in terms of age, underlying diseases, weight status and presence of comorbidities. Furthermore, the post-transplant period encompasses several stages characterized by physiological and pathophysiological changes that affect nutritional status of patients and necessitate tailored nutrition management. We provide an overview of the current state of knowledge regarding nutritional requirements in the post-lung transplant period from the immediate post-operative phase to long-term follow-up. In the immediate post-transplantation phase, the high doses of immunosuppressants and corticosteroids, the goal of maintaining hemodynamic stability, the presence of a catabolic state, and the wound healing process increase nutritional demands and lead to metabolic perturbations that necessitate nutritional interventions. As time from transplantation increases, complications such as obesity, osteoporosis, cancer, diabetes, and kidney disease, may develop and require adjustments to nutrition management. Until specific nutritional guidelines for lung recipients are elaborated, recommendations regarding nutrient requirements are formulated to provide guidance for clinicians caring for these patients. Finally, the management of recipients with special considerations is also briefly addressed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".