ThromboLUX - The First Routine Platelet Transfusion Quality Test
Bibliographic record
Abstract
Prior to the ThromboLUX, no single in vitro test existed that could be used individually to evaluate the quality and effectiveness of platelet concentrates for transfusion. As hospitals cannot currently predict effective from ineffective transfusions prior to seeing patient responses, they purchase all platelet products. In North America every year over 3 million platelet transfusions are given to heart surgery, bleeding or cancer patients at a cost of at least 1.5 billion dollars. LightIntegra intends to make platelet quality testing a regular practice in blood banks around the world by making it accessible, affordable, reliable and fast. The ThromboLUX uses the principle of dynamic light scattering (DLS) to determine the kind of particles in the platelet concentrate, how many of the particles exist, and how they respond to temperature stress. The temperature response of platelets was a breakthrough discovery and combined with state-of-the-art laser and optics technology the ThromboLUX addresses the market need to provide a safe, quick and simple diagnostic test for platelet quality and function. Provision of the ThromboLUX results prior to transfusion, will allow hospitals to reduce costs. As fewer ineffective transfusions will occur, and thus fewer repeat transfusions, hospitals will reduce platelet costs as they will only be purchasing ‘good’ platelet concentrates. Additionally, patient care will improve as physicians will no longer have the uncertainty of unsatisfactory transfusion outcome and patients will no longer be exposed to a huge, under-recognized risk. Patient costs will also decrease due to the reduction in the length of hospital stays. The significant cost reduction gained by eliminating the use of ineffective platelets, improved efficiency, and the correlation of the ThromboLUX test results with transfusion outcome are strong incentives for the adoption of ThromboLUX.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".