Lymphocyte subset at time of Epstein‐Barr viremia post‐allogeneic hematopoietic stem cell transplantation in children may predict development of post‐transplant lymphoproliferative disease: CD8:CD20 ratio as a sensitive predictor
Bibliographic record
Abstract
EBV-associated PTLD following allogeneic HSCT is a serious complication associated with significant mortality. In this retrospective study, we evaluated whether lymphocyte subset numbers and CD8:CD20 ratio at time of EBV viremia in children undergoing allogeneic HSCT could predict development of PTLD. Absolute lymphocyte count, lymphocyte subsets, and CD8:CD20 ratio at the time of EBV viremia were analyzed. Patients who were treated preemptively with rituximab for high blood EBV viral load were excluded. Out of 266 patients transplanted during the study period, 26 patients were included in the analysis. Patients were divided into two cohorts; cohort 1 included patients with EBV-associated PTLD (n = 5; four with proven, one with probable PTLD). Cohort 2 included patients with EBV viremia without PTLD (n = 21). Lymphocyte recovery was slower in the PTLD group. CD8:CD20 ratio was significantly lower in the PTLD group (median 0.15) compared to the non-PTLD group (median 2.4, P = .012). Using the ROC curve and 1 as the cutoff value, CD8:CD20 ratios were analyzed. In the PTLD group, 4/5 patients (80%) had a ratio <1 whereas in the non-PTLD group, all 21 patients had a ratio >1. Sensitivity and specificity were 80% and 100%, respectively. Negative and PPVs were 95% and 100%, respectively. Profoundly low T-cell count and CD8:CD20 ratio may be used to predict development of PTLD in the context of EBV viremia in children post-allogeneic HSCT. Further studies are needed to validate this finding.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".