Outcome of hematopoietic stem cell transplantation for adenosine deaminase–deficient severe combined immunodeficiency
Bibliographic record
Abstract
Deficiency of the purine salvage enzyme adenosine deaminase leads to SCID (ADA-SCID). Hematopoietic cell transplantation (HCT) can lead to a permanent cure of SCID; however, little data are available on outcome of HCT for ADA-SCID in particular. In this multicenter retrospective study, we analyzed outcome of HCT in 106 patients with ADA-SCID who received a total of 119 transplants. HCT from matched sibling and family donors (MSDs, MFDs) had significantly better overall survival (86% and 81%) in comparison with HCT from matched unrelated (66%; P < .05) and haploidentical donors (43%; P < .001). Superior overall survival was also seen in patients who received unconditioned transplants in comparison with myeloablative procedures (81% vs 54%; P < .003), although in unconditioned haploidentical donor HCT, nonengraftment was a major problem. Long-term immune recovery showed that regardless of transplant type, overall T-cell numbers were similar, although a faster rate of T-cell recovery was observed after MSD/MFD HCT. Humoral immunity and donor B-cell engraftment was achieved in nearly all evaluable surviving patients and was seen even after unconditioned HCT. These data detail for the first time the outcomes of HCT for ADA-SCID and show that, if patients survive HCT, long-term cellular and humoral immune recovery is achieved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".