Assessing red blood cell product quality with 2,3‐ <scp>DPG</scp> , <scp>ATP</scp> and <scp>p50</scp> assays: A <scp>BEST</scp> Collaborative study
Bibliographic record
Abstract
To comply with regulatory guidelines, blood products undergo laboratory testing to assess red blood cell (RBC) quality during manufacturing, storage and delivery. Measurements of metabolites such as 2,3-diphosphoglycerate (2,3-DPG) have been relied upon as proxies for in vivo function, although their predictive value for clinical efficacy is not well substantiated. Following the discontinuation of the only validated commercial assay for 2,3-DPG, we reviewed existing literature and performed a retrospective analysis of datasets from two blood centres in North America to evaluate adenosine-5'-triphosphate (ATP) and p50 as alternatives. The literature did not provide sufficient evidence to support adopting p50 in place of 2,3-DPG. Although the assays are complementary, we found several exceptions in which biological and technical factors reduced the strength of correlation between 2,3-DPG and p50. ATP, another marker of RBC quality, was not well correlated either with 2,3-DPG or p50 in quality monitoring datasets or with in vivo circulation kinetics in healthy adults. Our assessment underscores the need for a replacement assay for 2,3-DPG for comprehensive characterization of novel red cell products and storage conditions.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".