Peripheral Blood Eosinophilia Has a Favorable Prognostic Impact on Transplant Outcomes after Allogeneic Peripheral Blood Stem Cell Transplantation
Bibliographic record
Abstract
Peripheral eosinophilia after allogeneic stem cell transplant (ASCT) may reflect the activation of the Th2 cytokine pathway. A retrospective analysis was performed to evaluate the impact of early- (before day 100: EEo) or late-onset (beyond day 100: LEo) eosinophilia (> or =0.5 x 10(9)/L in peripheral blood) on transplant outcomes after peripheral blood SCT (PBSCT) in 237 patients. The incidence of EEo and LEo was 43% at day 100 and 62% at 2 years, respectively. Compared with patients without LEo, improved transplant outcomes were observed in patients with LEo: better overall survival (OS; 86% versus 41%, P = 5 x 10(-11)), lower nonrelapse mortality (NRM; 10% versus 37%, P = 3 x 10(-6)), lower relapse incidence (11% versus 31%, P = 3 x 10(-5)), and higher GVHD-specific survival (GSS; 90% versus 64%, P = 1 x 10(-6)) were observed. In addition, similar finding was observed when transplant outcomes were analyzed according to the occurrence of eosinophilia at the onset of cGVHD. The multivariate analyses confirmed a favorable implication of LEo on OS, NRM, and GSS. LEo was associated with: (1) less severe chronic GVHD (cGVHD), (2) higher prevalence of autoantibodies, and (3) rapid lymphocyte count recovery after ASCT. In summary, the development of eosinophila after allogeneic PBSCT seemed to be a prognostic marker for improving transplant outcome.
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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 | 0.000 | 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) | 0.001 | 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".