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2: Anemia in pediatric intestinal failure: prevalence, etiology and predictors

2023· article· en· W4382056359 on OpenAlexaffabout
Jaclyn Strauss, Christina Belza, Glenda Courtney‐Martin, Dianna Yanchis, Yaron Avitzur, Jennifer deBruyn

Bibliographic record

VenueTransplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsAnemiaMedicineFerritinSoluble transferrin receptorEtiologyAnemia of chronic diseaseInternal medicineIron deficiencyIron-deficiency anemiaGastroenterologyHemoglobinPediatricsIron status

Abstract

fetched live from OpenAlex

Introduction: Anemia is an important health problem in children with intestinal failure (IF). It can contribute to neurodevelopmental delay and lower health related quality of life. Children with IF are at risk for iron deficiency anemia (IDA), anemia of inflammation (AI) and mixed IDA/AI. There is a paucity of data on the frequency of these types of anemias in children with IF and the contributions of the underlying etiologies is not well described. An improved understanding of the prevalence and causes of these anemias is needed to effectively treat and prevent anemia and its complications in children with IF. Methods: All children ages 6 months-18 years-old with intestinal failure, defined as the need for PN > 60 days due to intestinal disease or dysfunction, followed by the Group for Improvement of Intestinal Function and Treatment (GIFT) Program at SickKids Hospital between January 1, 2012 and December 31, 2021 were included. Children required a minimum of 1 complete blood count (CBC) and were excluded if they had intestinal transplant or had discontinued PN or follow-up before December 31, 2019. Data was retrieved longitudinally from electronic medical records. Anemia was defined by age-specific norms for hemoglobin; iron status was determined using soluble transferrin receptor (sTfR) and sTfR-ferritin index (sTfR-F). Frequencies of IDA (defined by anemia with ferritin <30 ug/L and elevated sTfR or sTfR-F>1.5), AI (anemia with ferritin > 100 ug/L and normal sTfR or sTfR-F <1) and mixed IDA/AI (anemia with ferritin 30-100 ug/L and elevated sTfR or sTfR-F>2) were determined. Period prevalence was calculated using the number of patients with anemia at any time during the 10-year study as the numerator and total number of patients as the denominator. Persistent anemia was defined as having anemia on > 2 occasions in patients with anemia and >1 CBC measurement. Statistical analyses included χ2 and test of proportions. Results: Fifty-four children met inclusion criteria. Median age at end of study was 4.8 ([IQR] 2.7-8.3) years, 52% male and median duration of follow up 3.4 (1.1-6.6) years. Short bowel syndrome (SBS) was present in 74%. The period prevalence of anemia was 76%; of 38 children with anemia and >1 annual CBC, 74% had persistent anemia. Of those with anemia, 11 (27%) had IDA, 27 (66%) AI and 7 (17%) mixed anemia; 7 children had >1 subtype of anemia. There was no difference in prevalence of anemia subtypes based on SBS status. Conclusions: A high 10-year period prevalence of anemia (76%) was found in this study. Only 27% had IDA, compared to 66% with AI. The relatively lower rate of IDA may be a result of routine iron supplementation in TPN. Comparison of this single center data to other Canadian centers (study ongoing) will provide an additional insight into the connection between methods of iron supplementation and frequency of anemia.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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