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Record W2618814323 · doi:10.1111/petr.12944

Diet quality of children post‐liver transplantation does not differ from healthy children

2017· article· en· W2618814323 on OpenAlexaff
Abeer Salman Alzaben, Krista MacDonald, Cheri Robert, Andrea M. Haqq, Susan Gilmour, Jason Yap, Diana R. Mager

Bibliographic record

VenuePediatric Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsStollery Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineAnthropometryMicronutrientFructoseAdded sugarObesityInternal medicinePediatricsGastroenterologyFood science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.323
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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