Characterization of Platelet Concentrates Using Dynamic Light Scattering
Bibliographic record
Abstract
BACKGROUND: Each year, millions of platelet transfusions save the lives of cancer patients and patients with bleeding complications. However, between 10 and 30% of all platelet transfusions are clinically ineffective as measured by corrected count increments, but no test is currently used to identify and avoid these transfusions. ThromboLUX(®) is the first platelet test intended to routinely characterize platelet concentrates prior to transfusion. METHODS: ThromboLUX is a non-invasive, optical test utilizing dynamic light scattering to characterize a platelet sample by the relative quantity of platelets, microparticles, and other particles present in the sample. ThromboLUX also determines the response of platelets to temperature changes. From this information the ThromboLUX score is calculated. Increasing scores indicate increasing numbers of discoid platelets and fewer microparticles. ThromboLUX uses calibrated polystyrene beads as a quality control standard, and accurately measures the size of the beads at multiple temperatures. RESULTS: Results from apheresis concentrates showed that ThromboLUX can determine the microparticle content in unmodified samples of platelet concentrates which correlates well with the enumeration by flow cytometry. ThromboLUX detection of microparticles and microaggregates was confirmed by microscopy. CONCLUSION: ThromboLUX provides a comprehensive and novel analysis of platelet samples and has potential as a noninvasive routine test to characterize platelet products to identify and prevent ineffective transfusions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".