Routine Screening Method for Microparticles in Platelet Transfusions
Bibliographic record
Abstract
Platelet inventory management based on screening microparticle content in platelet concentrates is a new quality improvement initiative for hospital blood banks. Cells fragment off microparticles (MP) when they are stressed. Blood and blood components may contain cellular fragments from a variety of cells, most notably from activated platelets. When performing their roles as innate immune cells and major players in coagulation and hemostasis, platelets change shape and generate microparticles. With dynamic light scattering (DLS)-based microparticle detection, it is possible to differentiate activated (high microparticle) from non-activated (low microparticle) platelets in transfusions, and optimize the use of this scarce blood product. Previous research suggests that providing non-activated platelets for prophylactic use in hematology-oncology patients could reduce their risk of becoming refractory and improve patient care. The goal of this screening method is to routinely differentiate activated from non-activated platelets. The method described here outlines the steps to be performed for routine platelet inventory management in a hospital blood bank: obtaining a sample from a platelet transfusion, loading the sample into the capillary for DLS measurement, performing the DLS test to identify microparticles, and using the reported microparticle content to identify activated platelets.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".