Energy Requirements of a Pediatric Lung Transplant Population
Bibliographic record
Abstract
Background: Comparison of estimated energy requirements using predictive equations (PBMR) to measured resting energy requirements (MREE) using indirect calorimetry is not widely investigated in the pediatric lung transplant (LTx) population. In this population, optimal nutrition delivery may promote appropriate weight gain post-transplantation. The purpose of this case series was to evaluate the difference between predicted and measured energy requirements pre- and post- transplant by comparing PBMR calculated by the World Health Organization (WHO) equation and MREE by indirect calorimetry. The secondary aim was to determine if the diagnosis of Cystic Fibrosis (CF) influenced energy expenditure. It was predicted that the majority of LTx children would have higher MREE than PBMR (denoted as % PBMR). The % PBMR value is relative to MREE. Methods and findings: After exclusion, there were 7 LTx patients (n=5 male, n=4 CF, median age 11.55, age range: 4-16 y) and 14 measurements (7 repeated measures). Measurements were conducted pre- or post- transplant. Pre-LTx patients (n=4 patients, n=2 CF) exhibited a median 106% PBMR (interquartile range: 24%). Post-LTx patients (n=3 patients, n=2 CF) exhibited a median 105% PBMR (24%). The median % PBMR did not differ significantly between CF and non-CF LTx children irrespective of transplant status (113% (22%), 103% (19%), respectively, P=0.12). Conclusion: These results suggest that MREE was higher than PBMR; the WHO equation may underestimate energy requirements for pre- and post-LTx children. There was a trend towards decreasing energy requirements post-LTx. In children post-LTx, the diagnosis of CF may not affect energy requirements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".