Quantitative and qualitative differences in use and trends of hematopoietic stem cell transplantation: a Global Observational Study
Bibliographic record
Abstract
Fifty-five years after publication of the first hematopoietic stem cell transplantation this technique has become an accepted treatment option for defined hematologic and non-hematologic disorders. There is considerable interest in understanding differences in its use and trends on a global level and the macro-economic factors associated with these differences. Data on the numbers of hematopoietic stem cell transplants performed in the 3-year period 2006-2008 were obtained from Worldwide Network for Blood and Marrow Transplantation member registries and from transplant centers in countries without registries. Population and macro-economic data were collected from the World Bank and from the International Monetary Fund. Transplant rates were analyzed by indication, donor type, country, and World Health Organization regional offices areas and related to selected health care indicators using single and multiple linear regression analyses. Data from a total of 146,808 patients were reported by 1,411 teams from 72 countries over five continents. The annual number of transplants increased worldwide with the highest relative increase in the Asia Pacific region. Transplant rates increased preferentially in high income countries (P=0.02), not in low or medium income countries. Allogeneic transplants increased for myelodysplasia, chronic lymphocytic leukemia, acute leukemias, and non-malignant diseases but decreased for chronic myelogenous leukemia. Autologous transplants increased for autoimmune and lymphoproliferative diseases but decreased for leukemias and solid tumors. Transplant rates (P<0.01), donor type (P<0.01) aand disease indications (P<0.01) differed significantly between countries and regions. Transplant rates were associated with Gross National Income/capita (P<0.01) but showed a wide variation of explanatory content by donor type, disease indication and World Health Organization region. Hematopoietic stem cell transplantation activity is increasing worldwide. The preferential increase in high income countries, the widening gap between low and high income countries and the significant regional differences suggest that different strategies are required in individual countries to foster hematopoietic stem cell transplantation as an efficient and cost-effective treatment modality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".