Hematopoietic Stem-Cell Transplantation in the Resource-Limited Setting: Establishing the First Bone Marrow Transplantation Unit in Bangladesh
Bibliographic record
Abstract
PURPOSE: Treatment of malignant and nonmalignant hematologic diseases with hematopoietic stem-cell transplantation (HSCT) was first described almost 60 years ago, and its use has expanded significantly over the last 20 years. Whereas HSCT has become the standard of care for many patients in developed countries, the significant economic investment, infrastructure, and health care provider training that are required to provide such a service have prohibited it from being widely adopted, particularly in developing countries. METHODS: Over the past two decades, however, efforts to bring HSCT to the developing world have increased, and several institutions have described their efforts to establish such a program. We aim to provide an overview of the current challenges and applications of HSCT in developing countries as well as to describe our experience in developing an HSCT program at Dhaka Medical College and Hospital in Bangladesh via a partnership with health care providers at Massachusetts General Hospital. RESULTS AND CONCLUSION: We discuss key steps of the program, including the formation of a collaborative partnership, infrastructure development, human resource capacity building, and financial considerations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".