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Record W3007258182 · doi:10.1093/jcag/gwz047.255

A256 TRENDS IN THE PREVALENCE AND SEVERITY OF ANEMIA IN PEDIATRIC PATIENTS WITH INFLAMMATORY BOWEL DISEASE IN THE LAST DECADE

2020· article· en· W3007258182 on OpenAlexaff
Shanshan Geng, Zainab Ridha, Leah Pham, Eric Tran, A. Peixoto, S A Tchogna, Colette Deslandres, Prévost Jantchou

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineAnemiaInflammatory bowel diseaseIncidence (geometry)Internal medicineUlcerative colitisDiseaseAnemia of chronic diseaseIron-deficiency anemiaHemoglobinGastroenterologyPediatrics

Abstract

fetched live from OpenAlex

Abstract Background Anemia is one of the most common extraintestinal manifestations in patients with inflammatory bowel disease (IBD) at diagnosis. Studies have shown that anemia was associated with low levels of quality of life, which improves with the correction of anemia in adults. Recent data have shown an increase in the incidence and severity of pediatric IBD. Aims To investigate the prevalence of anemia in children at diagnosis of IBD and the trends in the past decade. The secondary aim was to investigate the associations between hemoglobin (Hb) levels and disease characteristics. Methods Eligible patients (age ≤18 years, diagnosed with IBD from 2009 to 2018) were retrospectively identified through our IBD database. Disease localization and phenotype were defined according to the Paris Classification of IBD. Anemia was defined by Hb levels according to WHO targets. The annual prevalence of anemia was calculated according to subtype (inflammatory vs iron deficiency). The Pediatric Crohn’s Disease Activity Index (PCDAI) and the Pediatric Ulcerative Colitis Activity Index (PUCAI) were used to assess the disease severity at diagnosis. Results We included 887 patients (439 females), mean (SD) age of 13.1 (3.4) years. Of these, 519 (58.5%) were identified with anemia within 30 days of diagnosis. The median (IQR) Hb level was 108 (98 -114) g/dL. Severe anemia (< 70 g/dL) was present in 1.8 % of patients. The prevalence of anemia at diagnosis remained relatively stable ranging from 60.2% in 2009 to 60.4% in 2018. The annual proportion of inflammatory vs iron-deficiency anemia is displayed in figure 1. Anemia was more prevalent in Crohn’s disease (CD) (62.2%) than Ulcerative colitis (UC) (57.9%) or IBD-unclassified (39.6%). The disease severity scores were higher in those with anemia. The median (IQR) PCDAI and PUCAI were respectively 37.5 (27.5–47.5) and 55.0 (40.0–65.0) in the anemic group as compared to 27.5 (20.0–37.50) and 35.0 (25.0–55.0) in the non-anemic group; P<0.0001. Patients with anemia had a lower BMI z-score [median (IQR) -0.84 (-1.84 - 0.08)] than the non-anemic patients [median (IQR) -0.38 (-1.21 - 0.43)]; P<0.001. The prevalence of anemia correlated significantly with disease location: upper intestinal involvement [L4a(67.7%) L4b(63.6%) L4aL4b(60.7%) none (52.8%)] P = 0.024 for CD; for UC [E1(21.1%) E2(44.4%) E3(75.0%) E4 (71.1%)] P<0.0001. A moderate correlation was found between Hb levels and C-reactive protein (r= -0.312, 95% CI: -0.378 to -0.243, P<0.0001). Conclusions Anemia remains a prevalent symptom in pediatric patients with IBD, and it is correlated with the extent of intestinal involvement and disease severity. The impact of anemia at Diagnosis and during follow-up on the levels of quality of life and physical activity is currently under investigation. Funding Agencies None

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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