Comparison of the platelet activation status of single‐donor platelets obtained with two different cell separator technologies
Bibliographic record
Abstract
BACKGROUND: The microparticle content (MP%) of apheresis platelets-a marker of platelet activation-is influenced by donor factors and by external stressors during collection and storage. This study assessed the impact of apheresis technology and other factors on the activation status (MP%) of single-donor apheresis platelets. STUDY DESIGN AND METHODS: Data from six US hospitals that screened platelets by measuring MP% through dynamic light scattering (ThromboLUX) were retrospectively analyzed. Relative risks (RRs) were derived from univariate and multivariable regression models, with activation rate (MP% ≥15% for plasma-stored platelets; ≥10% for platelet additive solution [PAS]-stored platelets) and MP% as outcomes. Apheresis platform (Trima Accel vs Amicus), storage medium (plasma vs PAS), pathogen reduction, storage time, and testing location were used as predictors. RESULTS: Data were obtained from 7511 platelet units collected using Trima (from 16 suppliers, all stored in plasma, 20.0% were pathogen-reduced) and 2456 collected using Amicus (from four different collection facilities of one supplier, 65.0% plasma-stored, 35.0% PAS-stored, none pathogen-reduced). Overall, 30.0% of Trima platelets were activated compared to 45.6% of Amicus platelets (P < .0001). Multivariable analysis identified apheresis platform as significantly associated with platelet activation, with a lower activation rate for Trima than Amicus (RR: 0.641, 95% confidence interval [CI]: 0.578; 0.711, P < .0001) and a 6.901% (95% CI: 5.926; 7.876, P < .0001) absolute reduction in MP%, when adjusting for the other variables. CONCLUSION: Trima-collected platelets were significantly less likely to be activated than Amicus-collected platelets, irrespective of the storage medium, the use of pathogen reduction, storage time, and testing site.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".