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IRON DEFICIENCY ANAEMIA: REFLECTING SCIENTIFIC PROGRESS AND TRENDS THROUGH BIBLIOMETRIC ANALYSIS

2025· article· uk· W4414675235 on OpenAlexaboutno aff
Tetiana Chernets, Iryna Savchenko, Taras Krytsky, Oksana Khlibovska

Bibliographic record

VenueПерспективи та інновації науки · 2025
Typearticle
Languageuk
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsScientific progressIron deficiencyBibliometricsTrend analysisTechnical progress

Abstract

fetched live from OpenAlex

Iron deficiency anaemia is one of the most common forms of anaemia and a serious global health problem.This condition occurs when the body's iron stores are insufficient to meet physiological needs, leading to impaired haemoglobin synthesis and reduced oxygen delivery to tissues.The causes of IDA are varied, including chronic blood loss, reduced iron absorption, insufficient intake or increased body requirements, as well as 'functional' deficiency due to elevated hepcidin levels in chronic diseases.Clinical manifestations of IDA include weakness, fatigue, Журнал «Перспективи та інновації науки» (Серія «Педагогіка», Серія «Психологія», Серія «Медицина») № 9(55) 2025 1683 shortness of breath, pale skin, as well as appetite disorders such as pica and restless legs syndrome.In children, this can lead to delayed psychomotor development, and in pregnant women, to the risk of complications during childbirth.Diagnosis of IDA is often difficult due to the non-specificity of symptoms and the possibility of falsely normal ferritin levels in inflammatory processes.Treatment usually includes oral iron supplements, although their tolerability is limited by side effects.Intravenous preparations are used in cases of intolerance or malabsorption, but they require additional resources and safety monitoring.According to the bibliometric analysis conducted in this article, there has been a significant increase in the number of publications on the topic of ZDA since 2010, confirming the scientific community's high interest in this issue.The main areas of research include new therapeutic approaches, such as personalised medicine, optimisation of iron doses and regimens, and the role of hepcidin in iron regulation in the body.Bibliometric analysis has shown that the leading countries in IDA research are the United States, the United Kingdom, India, and Italy, and the leading institutions are Harvard Medical School and the University of Toronto.Active support for research from organisations such as the National Institutes of Health (NIH) and the National Natural Science Foundation of China was also noted.Given the high prevalence and impact of IDA, this topic remains relevant and requires further research in the areas of diagnosis, treatment and prevention, particularly in the context of global strategies to combat iron deficiency.

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)0.013
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.082
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.1730.283
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.034
GPT teacher head0.374
Teacher spread0.340 · 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.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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