Plateletpheresis-associated lymphopenia in frequent platelet donors
Bibliographic record
Abstract
Abstract More than 1 million apheresis platelet collections are performed annually in the United States. After 2 healthy plateletpheresis donors were incidentally found to have low CD4+ T-lymphocyte counts, we investigated whether plateletpheresis causes lymphopenia. We conducted a cross-sectional single-center study of platelet donors undergoing plateletpheresis with the Trima Accel, which removes leukocytes continuously with its leukoreduction system chamber. We recruited 3 groups of platelet donors based on the total number of plateletpheresis sessions in the prior 365 days: 1 or 2, 3 to 19, or 20 to 24. CD4+ T-lymphocyte counts were <200 cells per microliter in 0/20, 2/20, and 6/20 donors, respectively (P = .019), and CD8+ T-lymphocyte counts were low in 0/20, 4/20, and 11/20 donors, respectively (P < .001). The leukoreduction system chamber’s lymphocyte-extraction efficiency was ∼15% to 20% for all groups. Immunophenotyping showed decreases in naive CD4+ T-lymphocyte and T helper 17 (Th17) cell percentages, increases in CD4+ and CD8+ effector memory, Th1, and regulatory T cell percentages, and stable naive CD8+ and Th2 percentages across groups. T-cell receptor repertoire analyses showed similar clonal diversity in all groups. Donor screening questionnaires supported the good health of the donors, who tested negative at each donation for multiple pathogens, including HIV. Frequent plateletpheresis utilizing a leukoreduction system chamber is associated with CD4+ and CD8+ T-cell lymphopenia in healthy platelet donors. The mechanism may be repeated extraction of these cells during plateletpheresis. The cytopenias do not appear to be harmful.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".