Could Microparticles Be the Universal Quality Indicator for Platelet Viability and Function?
Bibliographic record
Abstract
High quality means good fitness for the intended use. Research activity regarding quality measures for platelet transfusions has focused on platelet storage and platelet storage lesion. Thus, platelet quality is judged from the manufacturer's point of view and regulated to ensure consistency and stability of the manufacturing process. Assuming that fresh product is always superior to aged product, maintaining in vitro characteristics should preserve high quality. However, despite the highest in vitro quality standards, platelets often fail in vivo. This suggests we may need different quality measures to predict platelet performance after transfusion. Adding to this complexity, platelets are used clinically for very different purposes: platelets need to circulate when given as prophylaxis to cancer patients and to stop bleeding when given to surgery or trauma patients. In addition, the emerging application of platelet-rich plasma injections exploits the immunological functions of platelets. Requirements for quality of platelets intended to prevent bleeding, stop bleeding, or promote wound healing are potentially very different. Can a single measurable characteristic describe platelet quality for all uses? Here we present microparticle measurement in platelet samples, and its potential to become the universal quality characteristic for platelet production, storage, viability, function, and compatibility.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".