Similarities and Differences Between Allogeneic Hematopoietic Cell and Organ Transplantation and What We Can Learn From Each Other to Guide Global Health Strategy
Bibliographic record
Abstract
BACKGROUND: Allogeneic hematopoietic cell transplantation (HCT) and solid organ transplantation (SOT) have evolved into successful, curative treatments for many severe congenital and acquired diseases. Both use medical products of human origin and should therefore have overarching regulatory frameworks. Both require critical decisions about donor selection, donor/recipient matching, immunosuppression, and long-term care, all tasks best performed by a trained, highly specialized multidisciplinary team. Both need committed institutions and governmental support for their success. Whereas the main barrier for performing SOT is the lack of suitable organs, access to a transplant center is the main limitation for HCT, which remains a highly specialized, complex, resource-intensive, and costly medical procedure. METHODS AND RESULTS: Here, we describe the main indications for HCT and SOT, their similarities and differences regarding donor selection, treatment prior to transplant, intensity and duration of immunosuppression after transplantation, their main complications, and consequences of donation for living donors. CONCLUSIONS: Strategies to improve worldwide access to HCT and SOT are discussed, as well as future developments in this highly innovative field of medicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".