The Effect of Smoking on Allogeneic Transplant Outcomes
Bibliographic record
Abstract
Using the Center for International Blood and Marrow Transplant Research (CIBMTR) data, we compared the transplant outcomes of patients with chronic myelogenous leukemia (CML) who were nonsmokers (NS) and past or current smokers (PCS). There were 2193 NS and 625 PCS who received matched sibling and unrelated donor allografts for CML in first chronic phase. We looked for dose effects and identified low and high dose smoking groups (>10 pack years, >1 pack per day). Outcomes were adjusted for known prognostic variables including the European Group for Blood and Marrow Transplant (EBMT) risk score. In multivariate analyses of sibling allograft recipients, relapse risk (RR) was higher (RR=1.67, P=.003) in smokers than NS, but the dose effects were not consistent. High-dose smokers experienced a 50% treatment-related mortality (TRM) versus 28% in the NS group at 5 years on univariate analysis, and the RR was 1.57 (P=.005) on multivariate analysis. Overall survival (OS) at 5 years was 68% in NS versus 62% in the low-dose smoking group versus 50% in the high-dose smoking group (P < .001). Smoking did not significantly affect outcomes in unrelated donor recipients, but numbers were smaller. High-dose smoking is associated with a reduction in OS in patients having sibling allografts for CML. A prospective study with detailed demographic, pulmonary function, and quality-of-life data would improve our understanding of this issue.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".