Distinctive clinical characteristics and favorable outcomes in patients with large granular lymphocytosis after allo‐<scp>HCT</scp>: 12‐year follow‐up data
Bibliographic record
Abstract
An increase in large granular lymphocytes (LGL) is frequently seen in patients following allogeneic hematopoietic cell transplantation (allo-HCT) and it has been associated with better outcomes in some reports. We assessed 826 consecutive patients at our institution with over 12 years of follow-up for the occurrence of LGL lymphocytosis after allo-HCT. The 3-year cumulative incidence of LGL lymphocytosis was 14.5% with a median duration of over 3.5 years. The development of LGL lymphocytosis was strongly correlated with CMV viremia and GVHD. The clinical course of patients with LGL lymphocytosis after allo-HCT was indolent, with the majority of these patients not displaying any clinical signs or symptoms related to the LGL proliferation. LGL lymphocytosis was associated with better outcomes, including higher overall survival (OS 86.6% vs 44.7% at 3 years), lower non-relapse mortality (NRM 5.5% vs 30.4% at 3 years), and lower risk of relapse (8.9% vs 22.9% at 3 years). A time-dependent multivariable analysis confirmed the favorable impact of LGL lymphocytosis on OS and NRM, but not on the risk of relapse. In multivariable analysis, a longer duration of LGL lymphocytosis was associated with better OS and NRM. Improved immunomodulatory properties of these cells, regulating GVHD and infections, may explain the observed favorable outcomes of patients who developed LGL lymphocytosis following allo-HCT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".