Assessment of donor cell engraftment after hematopoietic stem cell transplantation for sickle cell disease: A review of current and future methods
Bibliographic record
Abstract
Hematopoietic stem cell transplantation (HSCT) is the only established curative treatment for sickle cell disease (SCD), a debilitating red blood cell (RBC) disorder with significant prevalence worldwide. Accurate assessment of RBC engraftment following HSCT is essential to evaluate the status of the graft and can enable early intervention to treat or prevent graft rejection. Currently, chimerism measurement is performed on whole blood samples, which mainly reflect white blood cell (WBC) chimerism. This approach has limitations in assessing engraftment in patients with SCD because RBCs engraft non-linearly with WBCs. Direct measures of RBC chimerism exist but are not routinely used. In this review, we critically examine the current methodologies for assessing donor engraftment; highlight the limitations of these different methods, and present emerging and novel technologies with the potential to improve clinical monitoring of RBC engraftment post-HSCT for SCD. Promising alternative methodologies include RBC-specific flow cytometry, RBC-specific RNA analysis, and quantification of plasma cell-free DNA derived specifically from nucleated RBCs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".