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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".