P‐BB‐40 | Hospital and Blood Center Collaboration to Improve Platelet Delivery Efficiency: Increased Inventory Without Increased Wastage
Bibliographic record
Abstract
Study Design/Methods: Thirty previously cryopreserved RCCs, documented as having experienced at least one TWE, were selected and classified according to exposure event: (1) > À65°C for 34 min (n = 5), (2) > À65°C for approximately 2 days reaching a peak temperature of À30°C (n = 23), and (3) exposure to both Event 1 and Event 2 (n = 2).Ten previously cryopreserved RCCs documented as having no TWE were selected as controls.All RCCs were thawed (37°C), deglycerolized and resuspended in AS-3 using the ACP 215 Cell Processor.Units were stored hypothermically and tested at 0, 1, 7, and 14 days post-deglycerolization for RBC quality using an extensive panel of in vitro tests, including hemoglobin (Hb) content, RBC hemolysis, adenosine triphosphate (ATP), RBC indices, and RBC deformability.Multiple group comparisons were performed using Sidak's multiple comparisons test.Results/Findings: All RCC units, regardless of TWE exposure, showed no significant differences in quality parameters over 14 days of hypothermic storage.Furthermore, at all time points, all RCCs had a hematocrit <80%, hemoglobin per unit >35 g/unit, and hemolysis <0.8% in 85% of the units tested.Conclusions: Our results indicate that exposures to realworld TWEs did not significantly impact the quality of RCCs post-deglycerolization.Further assessments and validation will need to be undertaken by blood centers to determine the impact of the frequency and duration of TWEs on product quality to help define more evidencebased criteria.With these criteria, blood centers could retain valuable and potentially rare RCCs that current blood inventory management strategies require to be discarded after even one exposure to storage temperatures warmer than À65°C. P-BB-39 | Frequency of Vasovagal Reactions with Large-Volume Plasma Donations in Canada
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".