Therapeutic Effectiveness of Nutrition Therapy in Pediatric Patients with Chronic Liver Diseases Awaiting Liver Transplantation
Bibliographic record
Abstract
It is important to prevent protein/calorie malnutrition in children with end stage liver diseases prior to transplantation. This study involved 34 patients between the ages of 10 and 156 months (mean value 25.69 months ± 32.2) (13 females and 21 males) on the liver transplant waiting list. Data collected as of three months before transplant and up to ten months after the procedure concerned gender, age, weight, height, Pediatric End Stage Liver Disease Score, baseline pathology, type of nutrition, type of transplant, immunosuppression, pulse steroid therapy, length of stay, and post transplant complications. Linear regression analysis showed that the length of hospital stay was 24.5 days more for females than for males, but also that intensive nutrition therapy shortens this stay for both female patients (P = 0.085) and younger patients (P = 0.023). The study population was divided into two groups according to the different nutritional therapies adopted. The Student’s t-test and Mann-Whitney test evidenced that the group receiving intensive nutrition therapy grew taller compared with the group following an oral diet (mean -1.37 and Prob = 0.043); that females grew taller compared to males (mean -1.65 +/- 0.56); and that there was an increase in height among the children in the group receiving intensive nutrition therapy despite the presence (-1.37 +/- 0.56) or absence (-14.8 +/- 5.44 and Prob = 0.035) of complications, and despite the administration (-1.03 +/- 0.33) or non administration (-1.48 +/- 0.55 and Prob = 0.019) of steroids. Intensive nutrition therapy enhances the velocity of growth in height and shortens the length of hospital stay, thus optimizing the final prognosis of the baseline pathology.
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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 | 0.000 | 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".