MétaCan
Menu
Back to cohort
Record W2110722044 · doi:10.1111/petr.12527

Vitamin D insufficiency and deficiency in pediatric renal transplant recipients

2015· article· en· W2110722044 on OpenAlexaff
Kirsten Ebbert, Josephine Chow, Jennifer Krempien, Mina Matsuda‐Abedini, Janis M. Dionne

Bibliographic record

VenuePediatric Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsHospital for Sick ChildrenUniversity of British ColumbiaUniversity of TorontoBC Children's Hospital
Fundersnot available
KeywordsMedicineInternal medicineVitamin D and neurologyRenal transplantPopulationTransplantationvitamin D deficiencyKidney transplantationGastroenterologyVitaminPediatricsPhysiologyEnvironmental health

Abstract

fetched live from OpenAlex

Vitamin D deficiency is prevalent in the pediatric CKD population. Recognizing that renal transplant recipients have CKD, we assessed the prevalence of vitamin D insufficiency and deficiency in pediatric renal transplant recipients, compared to a healthy pediatric population. We prospectively studied 25(OH)D levels in 29 pediatric renal transplant recipients and 45 control patients over one yr. The overall prevalence of vitamin D insufficiency and deficiency was common in both populations, at 76% (95% CI: 61, 87%) in the pediatric renal transplant recipients and 91% (95% CI: 80, 98%) in the control group. In the paired renal transplant samples, the mean 25(OH)D level was 52.3 ± 17.9 nmol/L in the winter and 65.6 ± 18.8 nmol/L in the summer (95% CI diff.: 3.9, 22.7), in keeping with a significant seasonal difference. The mean dietary intake of vitamin D in the renal transplant recipients, assessed by three-day dietary record, was 5.7 μg/day, with a vitamin D intake below the EAR in the majority. We did not find an association between vitamin D intake and 25(OH)D levels in this study, likely due to the low dietary intake of vitamin D within the transplant population, identifying a potential area for intervention and improvement.

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)0.001
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 score0.025
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePediatric TransplantationSame topicVitamin D Research StudiesFrench-language works237,207