Characterization of Platelet Concentrates Using Dynamic Light Scattering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it