Immune reconstitution and survival of 100 SCID patients post–hematopoietic cell transplant: a PIDTC natural history study
Bibliographic record
Abstract
= .009). Other factors, including the diagnosis of typical vs leaky SCID/Omenn syndrome, diagnosis via family history or newborn screening, use of preparative chemotherapy, or the type of donor used, did not impact survival. Although 1-year post-HCT median CD4 counts and freedom from IV immunoglobulin were improved after the use of preparative chemotherapy, other immunologic reconstitution parameters were not affected, and the potential for late sequelae in extremely young infants requires additional evaluation. After a T-cell-replete graft, landmark analysis at day +100 post-HCT revealed that CD3 < 300 cells/μL, CD8 < 50 cells/μL, CD45RA < 10%, or a restricted Vβ T-cell receptor repertoire (<13 of 24 families) were associated with the need for a second HCT or death. In the modern era, active infection continues to pose the greatest threat to survival for SCID patients. Although newborn screening has been effective in diagnosing SCID patients early in life, there is an urgent need to identify validated approaches through prospective trials to ensure that patients proceed to HCT infection free. The trial was registered at www.clinicaltrials.gov as #NCT01186913.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".