Global Trends and Scientific Contributions in Thalassemia Research (2015–2024): An Integrative Bibliometric and Meta-Analysis of Diagnostic, Genetic and Treatment Approaches
Bibliographic record
Abstract
ABSTRACT: Thalassemia is a severe hereditary disorder of hemoglobin synthesis, characterized by markedly reduced or absent production of functional hemoglobin molecules, leading to chronic anemia, progressive tissue hypoxia, and multi-organ complications. In its most severe form, patients require lifelong blood transfusions, predisposing them to iron accumulation, cardiac and hepatic dysfunction, endocrine abnormalities, and premature mortality without timely intervention. This study presents an integrative bibliometric and meta-analytical assessment of global thalassemia research from 2015 to 2024, with a focus on diagnostic innovation, molecular genetics, and therapeutic advancements.Bibliometric mapping revealed fluctuations in research productivity, with peaks in 2016 and 2018 and a marked decline in 2024. Scientific contributions originated from thirty-eight nations, with Germany producing the highest number of publications, the United States attaining the greatest citation impact, and India demonstrating the strongest strength in multinational collaboration. Network analysis positioned Germany, Austria, the United States, and Canada as central contributors, with the United States exhibiting the highest collaboration index. Influential researchers, including Ali T. Taher, Elliott P. Vichinsky, and Maria Domenica Cappellini, each averaged over forty-six citations per publication.The meta-analysis identified four primary thematic domains: genetic characterization, clinical management, hematological assessment, and diagnostic methodology. Among genetic approaches, CRISPR-Cas9 genome editing achieved the highest association with favorable outcomes, followed by next-generation sequencing. In disease management, hematopoietic stem cell transplantation and gene therapy demonstrated the strongest therapeutic associations with improved prognosis. Diagnostic platforms, including high-performance liquid chromatography and capillary electrophoresis, yielded a pooled odds ratio of 5.31 with negligible heterogeneity, indicating high diagnostic reliability. Findings underscore the pivotal role of global collaboration and technological innovation in advancing thalassemia research, while the recent decline in scholarly output highlights the urgent need for renewed funding and strategic prioritization to sustain progress and improve patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.048 | 0.079 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".