Retention of hemostatic and immunological properties of frozen plasma and <scp>COVID</scp>‐19 convalescent apheresis fresh‐frozen plasma produced and freeze‐dried in Canada
Bibliographic record
Abstract
Abstract Background Randomized clinical trial data show that early plasma transfusion may save lives among trauma patients. Supplying plasma in remote environments is logistically challenging. Freeze‐dried plasma (FDP) offers a possible solution. Study Design and Methods A Terumo BCT plasma freeze‐drying system was evaluated. We compared pooled frozen plasma (FP) units with derived Terumo BCT FDP (TFDP) units and pooled COVID‐19 convalescent apheresis fresh‐frozen plasma (CC‐AFFP) with derived CC‐TFDP units. Parameters measured were: coagulation factors (F) II; V; VII; VIII; IX; XI; XIII; fibrinogen; Proteins C (PC) and S (PS); antithrombin (AT); α2‐antiplasmin (α2AP); ADAMTS13; von Willebrand Factor (vWF); thrombin–antithrombin (TAT); D‐dimer; activated complement factors 3 (C3a) and 5 (C5a); pH; osmolality; prothrombin time (PT); and activated partial thromboplastin time (aPTT). Antibodies to SARS‐CoV‐2 in CC‐AFFP and CC‐TFDP units were compared by plaque reduction assays and viral protein immunoassays. Results Most parameters were unchanged in TFDP versus FP or differed ≤15%. Mean aPTT, PT, C3a, and pH were elevated 5.9%, 6.9%, 64%, and 0.28 units, respectively, versus FP. CC‐TFDP showed no loss of SARS‐CoV‐2 neutralization titer versus CC‐AFFP and no mean signal loss in most pools by viral protein immunoassays. Conclusion Changes in protein activities or clotting times arising from freeze‐drying were <15%. Although C3a levels in TFDP were elevated, they were less than literature values for transfusable plasma. SARS‐CoV‐2‐neutralizing antibody titers and viral protein binding levels were largely unaffected by freeze‐drying. In vitro characteristics of TFDP or CC‐TFDP were comparable to their originating plasma, making future clinical studies appropriate.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".