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Record W2460852892 · doi:10.1097/mph.0000000000000625

A Longitudinal Study of Growth and Relation With Anemia and Iron Overload in Pediatric Patients With Transfusion-dependent Thalassemia

2016· article· en· W2460852892 on OpenAlexaff
Kwannapas Nokeaingtong, Pimlak Charoenkwan, Suchaya Silvilairat, Suwit Saekho, Yupada Pongprot, Prapai Dejkhamron

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsAssociated Medical Services
Fundersnot available
KeywordsMedicineShort statureConfidence intervalInternal medicineThalassemiaFerritinAnemiaHemoglobinPediatricsOdds ratioGastroenterology

Abstract

fetched live from OpenAlex

Short stature is one of the most common endocrinopathies in transfusion-dependent thalassemia (TDT). This study aimed to determine the longitudinal pattern of growth in pediatric patients with TDT and study the relationship between growth and hemoglobin level, serum ferritin level/iron overload parameters, and other clinical factors. The interval height-for-age Z-scores (HAZ) of 50 patients with TDT, of a mean age of 13.3±2.8 years, were analyzed using linear mixed model analysis. Nineteen patients (38%) had short stature with HAZ≤-2.0. The prevalence of short stature increased with age. The estimated mean HAZ decreased by 0.19 SD per year from the age of 5 years until approximately 14 years (95% confidence interval [CI], -0.22 to -0.16, P<0.001). Male sex (estimate, -0.28; 95% CI, -0.43 to -0.14; P<0.001), mean 3-year hemoglobin level ≤8 g/dL (estimate, -0.36; 95% CI, -0.53 to -0.19; P<0.001), mean 3-year ferritin level ≥1800 ng/mL (estimate, -0.44; 95% CI, -0.59 to -0.29; P<0.001), and cardiac T2* ≤20 ms (estimate, -1.05; 95% CI, -1.34 to -0.77; P<0.001) were significantly associated with short stature. In conclusion, short stature in patients with TDT is common and relates significantly with increasing age, male sex, hemoglobin level, and iron overload status.

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 score0.036
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.248
Teacher spread0.241 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

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