Impact of hematopoietic stem cell transplantation on growth outcomes in mucopolysaccharidosis: a systematic review
Bibliographic record
Abstract
Mucopolysaccharidosis (MPS) is a group of genetic disorders characterized by defective lysosomal enzyme activity that can result in growth abnormalities and other complications. Hematopoietic stem cell transplantation (HSCT), especially bone marrow transplantation (BMT), aims to restore enzyme function and improve growth parameters in patients with MPS. This systematic review evaluates the impact of HSCT on growth outcomes, including height, weight, body mass index (BMI), head circumference, and pubertal development, in pediatric patients with MPS. Using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines, we systematically searched the PubMed, Embase, Scopus, and Web of Science databases. The retrieved studies focused on the growth outcomes of patients with MPS treated with HSCT emphasizing the role of BMT. Study quality was assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute checklist. The study protocol was registered in PROSPERO (registration no. CRD42024571488). These findings indicate that HSCT improves height, weight, and BMI, and better outcomes were observed in patients who underwent early transplantation. However, many patients still experience declining height z scores, resulting in short stature in adulthood, an elevated BMI, disproportionate head growth, and, in some cases, precocious puberty or pubertal arrest. Therefore, ongoing monitoring and personalized care are necessary to address these long-term growth challenges.
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 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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".