Diet quality of children post‐liver transplantation does not differ from healthy children
Bibliographic record
Abstract
Little has been studied regarding the diets of children following LTX. The study aim was to assess and compare dietary intake and DQ of healthy children and children post-LTX. Children and adolescents (2-18 years) post-LTX (n=27) and healthy children (n=28) were studied. Anthropometric and demographic data and two 24-hour recalls (one weekend; one weekday) were collected. Intake of added sugar, HFCS, fructose, GI, and GL was calculated. DQ was measured using three validated DQ indices: the HEI-C, the DGI-CA, and the DQI-I. Although no differences in weight-for-age z-scores were observed between groups, children post-LTX had lower height-for-age z-scores than healthy children (P<.01). With the exception of vitamin B12, no significant differences in energy and macronutrient (protein, carbohydrate, and fat), added sugar, HFCS, fructose, GI, GL, and micronutrient intakes and DQ indices (HEI-C, DGI-CA, and DQI-I) between groups were observed (P>.05). The majority of children in both groups (>40%) had low DQ scores. No significant interrelationships between dietary intake, anthropometric, and demographic were found (P>.05). Both healthy and children post-LTX consume diets with poor DQ. This has implications for risk of obesity and metabolic dysregulation, particularly in transplant populations on immunosuppressive therapies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".