Additive effects of blood donor smoking and gamma irradiation on outcome measures of red blood cell transfusion
Bibliographic record
Abstract
BACKGROUND: Recent publications have reported conflicting results regarding the role of blood donor tobacco use on hemoglobin (Hb) levels in patients after red blood cell (RBC) transfusion. We examined associations and interactions between donor, component, and recipient factors to better understand the impact of donor smoking on transfusion outcomes. STUDY DESIGN AND METHODS: We linked blood donor and component manufacturing data, including self-reported cigarette smoking, with a cohort of patients transfused RBCs between 2013 and 2016. Using multivariable regression, we examined Hb increments and subsequent transfusion requirements after single-unit RBC transfusion episodes, adjusting for donor, component, and recipient factors. RESULTS: We linked data on 4038 transfusion recipients who received one or more single-unit RBC transfusions (n = 5086 units) to donor demographic and component manufacturing characteristics. Among RBC units from smokers (n = 326), Hb increments were reduced after transfusion of gamma-irradiated units (0.76 g/dL; p = 0.033) but not unirradiated units (1.04 g/dL; p = 0.54) compared to those from nonsmokers (1.01 g/dL; n = 4760). In parallel with changes in Hb levels, donor smoking was associated with the receipt of additional RBC transfusions for irradiated (odds ratio [OR], 2.49; p = 0.01) but not unirradiated RBC units (OR, 1.10; p = 0.52). CONCLUSION: Donor smoking was associated with reduced Hb increments and the need for additional transfusions in recipients of gamma-irradiated RBC units. Additional research is needed to better understand interactions between donor, component, and recipient factors on efficacy measures of RBC transfusion.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".