A systematic literature review of incidence, mortality, and relapse of patients diagnosed with chronic graft versus host disease
Bibliographic record
Abstract
INTRODUCTION: Chronic graft-versus-host disease (chronic GVHD) is a leading cause of late death and contributes significantly to morbidity following hematopoietic stem cell transplantation. This study aims to provide a systematic literature review on incidence, mortality, and relapse of chronic GVHD patients. Areas covered: The authors searched for English-language articles published between 2007 and 2017 using PubMed. Studies that applied the 2005 or 2015 NIH Consensus Criteria for the diagnosis and staging of chronic GVHD, and had a cohort size of at least 100 patients were included. Expert opinion: The authors found a wide variation of incidence rates, which can be explained by heterogeneity in the characteristics of study samples and applied transplantation protocols. Chronic GVHD was associated with higher non-relapse mortality (NRM), superior overall survival (OS) and lower risk of relapse. Studies indicated an increased risk for NRM and worse OS in the presence of more severe disease. Future therapies should focus to reach a delicate balance between controlling disease severity among patients diagnosed with chronic GVHD and preserving the graft versus tumor effect which goes along with chronic GVHD and results in improved OS and decreased relapse rate. Nonetheless, factors predicting disease severity still need to be further understood.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".