Immune reconstitution after hematopoietic cell transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Successful immune reconstitution is important for decreasing posthematopoietic cell transplant (post-HCT) infections, relapse, and secondary malignancy, without increasing graft-versus-host disease (GVHD). Here we review how different parts of the immune system recover, and the relationship between recovery and clinical outcomes. RECENT FINDINGS: Innate immunity (e.g., neutrophils, natural killer cells) recovers within weeks, whereas adaptive immunity (B and T cells) recovers within months to years. This has been known for years; however, more recently, the pattern of recovery of additional immune cell subsets has been described. The role of these subsets in transplant complications like infections, GVHD and relapse is becoming increasingly recognized, as gleaned from studies of the association between subset counts or function and complications/outcomes, and from studies depleting or adoptively transferring various subsets. SUMMARY: Lessons learned from observational studies on immune reconstitution are leading to new strategies to prevent or treat posttransplant infections. Additional knowledge is needed to develop effective strategies to prevent or treat relapse, second malignancies and GVHD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".