Routine Quality Testing of Blood Platelet Transfusions with Dynamic Light Scattering
Bibliographic record
Abstract
Abstract Extension of the current 5‐day shelf life of platelet concentrates to increase the supply of this life saving blood product will require quality testing. However, no automated test exists to routinely measure the quality of platelet concentrates for transfusion. Platelet concentrates cannot be sampled and diluted. These practical limitations have prevented the routine use of optical methods for platelet quality testing. The Dynamic Light Scattering Platelet Monitor (DLS‐PM) addresses these limitations. The DLS‐PM is a portable instrument with a temperature‐controlled sample holder to accommodate a wide range of sample containers. The challenges of small sample size, short light path through the sample, and accurate temperature control have been solved. The DLS‐PM measures platelet size, number of platelet‐derived microparticles, and the response of platelets to temperature changes, which are combined to calculate a platelet quality score. In this paper we introduce the DLS‐PM and discuss the advantages and challenges for dynamic light scattering to become a clinically relevant, routinely used platelet test.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".