In vivo recovery and survival of apheresis and whole blood‐derived platelets: a paired comparison in healthy volunteers
Bibliographic record
Abstract
BACKGROUND: Methods of platelet preparation may alter the recovery and survival characteristics of platelets following transfusion. As suggested by a recent clinical trial, platelet recovery may be better preserved with apheresis platelet preparations than with platelets prepared from whole blood by the platelet-rich plasma (PRP) method. STUDY DESIGN AND METHODS: In vivo platelet recovery and survival of autologous leukoreduced (LR) apheresis platelets and autologous filter-LR PRP platelets were compared in 22 healthy volunteers using a paired crossover design. On the same day, each participant gave one apheresis platelet donation and one whole blood donation from which platelets were recovered from the PRP. The sequence of donations was randomly assigned for each participant. Following 5 days of storage and bacterial screening, a sample from each platelet product was labeled with either (51)chromium or (111)indium (randomly assigned) and both samples were simultaneously re-infused into the original donor. Recovery and gamma-function platelet survival were calculated for each platelet product using the multiple hit mathematical model. RESULTS: Five day stored LR-apheresis platelets had 18.8 percent better recovery, and 32.9 percent longer gamma-survival than filter-LR PRP platelets. Stored apheresis platelets had lower p-selectin expression and higher morphology scores than stored PRP platelets. CONCLUSIONS: Filter-LR PRP platelet preparation appears to adversely affect platelet recovery and survival characteristics. The reasons for this effect are not clear. These results may not apply to all apheresis and PRP methods of platelet preparation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".