Continuous and differential improvement in worldwide access to hematopoietic cell transplantation: activity has doubled in a decade with a notable increase in unrelated and non-identical related donors
Bibliographic record
Abstract
Promoting access to and excellence in hematopoietic cell transplantation (HCT) by collecting and disseminating data on global HCT activities is one of the principal activities of the Worldwide Network for Blood and Marrow Transplantation, a non-governmental organization in working relations with the World Health Organization. HCT activities are recorded annually by member societies, national registries and individual centers including indication, donor type (allogeneic/autologous), donor match and stem cell source (bone marrow/peripheral blood stem cells/cord blood). In 2018, 1,768 HCT teams in 89 countries (6 World Health Organization regions) reported 93,105 (48,680 autologous and 44,425 allogeneic) HCT. Major indications were plasma cell disorders and lymphoma for autologous, and acute leukemias and MDS/MPN for allogeneic HCT. HCT numbers increased from 48,709 in 2007. Notable increases were seen for autoimmune diseases in autologous and hemoglobinopathies in allogeneic HCT. The number of allogeneic HCT more than doubled with significant changes in donor match. While HCT from HLA-identical siblings has seen only limited growth, HCT from non-identical related donors showed significant increase worldwide. Strongest correlation between economic growth indicator of gross national income/capita and HCT activity/10 million population was observed for autologous HCT (correlation coefficient [r]=0.79). HCT from unrelated donors showed strong correlation (r=0.68), but only moderate correlation was detected from related donors (r=0.48 for HLA-identical sibling; r=0.45 for other). The use of HCT doubled in about a decade worldwide at different speed and with significant changes regarding donor match as a sign of improved access to HCT worldwide. Although narrowing, significant gaps remain between developing and non-developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".