One and a half million hematopoietic stem cell transplants: continuous and differential improvement in worldwide access with the use of non-identical family donors
Bibliographic record
Abstract
The Worldwide Network of Blood and Marrow Transplantation (WBMT) pursues the mission of promoting hematopoietic cell transplantation (HCT) for instance by evaluating activities through member societies, national registries and individual centers. In 2016, 82,718 first HCT were reported by 1,662 HCT teams in 86 of the 195 World Health Organization member states representing a global increase of 6.2% in autologous HCT and 7.0% in allogeneic HCT and bringing the total to 1,298,897 procedures. Assuming a frequency of 84,000/year, 1.5 million HCT were performed by 2019 since 1957. Slightly more autologous (53.5%) than allogeneic and more related (53.6%) than unrelated HCT were reported. A remarkable increase was noted in haploidentical related HCT for leukemias and lymphoproliferative diseases, but even more in non-malignant diseases. Transplant rates (TR; HCT/10 million population) varied according to region reaching 560.8 in North America, 438.5 in Europe, 76.7 in Latin America, 53.6 in South East Asia/Western Pacific (SEA/WPR) and 27.8 in African/East Mediterranean (AFR/EMR). Interestingly, haploidentical TR amounted to 32% in SEA/WPR and 26% in Latin America, but only 14% in Europe and EMR and 4.9% in North America of all allogeneic HCT. HCT team density (teams/10 million population) was highest in Europe (7.7) followed by North America (6.0), SEA/WPR (1.9), Latin America (1.6) and AFR/EMR (0.4). HCT are increasing steadily worldwide with narrowing gaps between regions and greater increase in allogeneic compared to autologous activity. While related HCT is rising, largely due to increase in haploidentical HCT, unrelated HCT is plateauing and cord blood HCT is in decline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".