Platelet components with persistent aggregates are more activated, which do not change following pre‐transfusion filtration: The <scp>BEST</scp> collaborative study
Bibliographic record
Abstract
BACKGROUND: Platelet components (PCs) with persistent aggregates are typically discarded. Few studies have characterized components with aggregates. A multicenter study was therefore conducted to determine whether PCs containing aggregates are more activated and whether donor attributes or processing methods influence aggregate formation. STUDY DESIGN AND METHODS: Seven international centers collected and tested apheresis PCs with persistent aggregates (n = 147), and controls without aggregates (n = 65). PCs were assigned a score (0-24), based on the size and number of aggregates. Platelet count, metabolism, activation markers (annexin-V, CD62P, cytokines, glycocalicin and prothrombin F1 + F2 fragments), extracellular vesicles (EVs), and function (collagen aggregation, TRAP-1, and ADP responses) were measured. A subset of PCs was filtered through a transfusion set. Donor attributes and processing methods were also assessed. Data were analyzed using a mixed model of linear regression. RESULTS: The median aggregate score in PCs with aggregates was 8. PCs with aggregates had a significantly lower glucose concentration, with significantly higher lactate levels. Platelets in PCs with aggregates were more activated, with significantly higher CD62P, annexin-V, and CD61/annexin-V-positive EVs, glycocalicin and prothrombin F1 + F2 fragments compared to controls. Although filtration removed aggregates, the activated platelet phenotype remained unchanged. PCs with aggregates came from older donors with a higher BMI, and from donors who had previously given donations with aggregates. CONCLUSIONS: Platelets in PCs with aggregates are markedly activated, and filtration does not change their phenotype. PCs containing many, large aggregates should be discarded, but those with fewer, small aggregates (aggregate score of 6 or less) could be retained.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".