Impact of graft‐versus‐host disease prophylaxis on second primary malignancies after allogeneic haematopoietic stem cell transplantation
Bibliographic record
Abstract
Second primary malignancies (SPMs) are a well-recognized late complication of allogeneic haematopoietic stem cell transplantation (HSCT). This study aims to evaluate the impact of anti-thymocyte globulin (ATG) and post-transplant cyclophosphamide (PTCy) combination on the incidence of SPMs, compared to other graft-versus-host disease (GvHD) prophylactic regimens. Among 1418 evaluable patients with a median follow-up of 6125 person-years, 138 patients developed an SPM. The cumulative incidence at 5 years was 10.6% (95% CI: 9-13). The use of ATG-PTCy was independently associated with a reduced risk of developing SPM (Hazard Ratio [HR], 0.65; p = 0.02), while older patient age (HR, 1.10; p = 0.03) and moderate-to-severe chronic GvHD (HR, 1.54; p = 0.02) were associated with an increased risk of SPM. Compared to the general population, HSCT recipients were 2.45 times more likely to develop a malignancy (p < 0.01). The 3-year overall survival from the time of SPM diagnosis was 69.8% (95% CI: 61-77) with haematological SPM independently associated with inferior survival (HR: 2.40; 95% CI: 1.3-4.5; p < 0.01). Fifteen patients (11%) developed more than one SPM. In conclusion, ATG-PTCy appears to reduce the risk of SPM post-HSCT. Active surveillance and screening for SPMs in transplant survivors are of paramount importance to ensure favourable outcomes.
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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 | 0.001 |
| 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".