The effectiveness of hematopoietic stem cell transplantation in treating pediatric sickle cell disease: Systematic review and meta-analysis
Bibliographic record
Abstract
Background: Patients with sickle cell disease (SCD) have just one recognized curative therapy option: hematopoietic stem cell transplantation (HSCT), which results in a long-lasting improvement in the clinical phenotype. Here, we assessed the effectiveness of HSCT in treating children with SCD by a systematic review and meta-analysis. Methods: Up until January 2024, a comprehensive search was done using Web of Science, CINAHL, Embase, Google Scholar, Cochrane Library, PubMed/Medline, and Embase. Two reviewers worked separately to extract the data, and Newcastle-Ottawa Quality Assessment tool was used to assess the research's quality. The outcomes analyzed were Overall survival (OS), event-free survival (EFS), graft failure (GF) and mortality. Results: Nineteen papers satisfied our inclusion requirements and were assessed to be of fair quality. The pooled rate of OS was high (92%; 95% CI: 90.3%-93.5%). Similar finding was detected for EFS (85.8%; 95% CI: 83.7%-87.7%). In the other hand, pooled rates of GF and mortality were 6.9% (95% CI: 5.3%-8.9%) and 7.4% (95% CI: 5%-10.7%), respectively. A significant publication bias was detected for OS, EFS and GF outcomes. Subgroups analysis showed that study design was the major source of heterogeneity. Conclusion: Our results show that HSCT is effective and safe, with pooled survival rates above 90%. It is important to assess innovative tactics in light of the alarming GF and mortality rates.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".