Cold‐stored platelets are effective in an in vitro model of massive transfusion protocol assessed by rotational thromboelastometry
Bibliographic record
Abstract
BACKGROUND: Platelets are a key component of massive transfusion in treating actively bleeding patients. While optimized for prophylactic transfusions, the effectiveness of the current standard room temperature stored platelets (RPs) in treating actively bleeding patients is not clear. Cold-stored platelets (CPs) have been shown to have superior hemostatic functions and the potential to extend shelf life. In this study, we explored the effect of using CPs versus RPs in an in vitro transfusion model based on the massive transfusion protocol. STUDY DESIGN AND METHODS: RPs or CPs were combined with RBCs and plasma in a 1:1:1 volume ratio to make transfusion packages. Whole blood was collected and then either diluted to 20% hematocrit or mixed with tPA (8.8 μg/ml). By volume, 70% of transfusion package was mixed with 30% whole blood to simulate massive transfusions and analyzed by rotational thromboelastometry. Transfusion package supernatant was analyzed for PAI-1 activity as well. RESULTS: Both transfusion packages restored the clot characteristics of hemodiluted or hyperfibrinolytic whole blood. Specifically, only transfusion packages made with CPs significantly reduced the maximum clot lysis of hyperfibrinolytic whole blood. PAI-1 activity in CPs transfusion packages were also significantly higher. DISCUSSION: Transfusion packages containing cold-stored platelets may be able to restore the blood hemostatic profile of bleeding patients. In addition, transfusion packages made from CPs may provide additional benefit of resisting hyperfibrinolysis in bleeding patients. In trauma where post-transfusion platelet recovery is less of a concern, CPs are a viable option to restore hemostasis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".