Platelet Function Testing: Quality Assurance
Bibliographic record
Abstract
Platelet function tests are widely used for the diagnosis of platelet disorders. In recent years there has been increasing interest in the use of platelet function tests to monitor antiplatelet drug therapy. Quality assurance is important to optimize the performance of laboratory assays but it has not been widely applied to platelet function tests. This deficiency likely reflects the need to use freshly collected blood samples for platelet function tests, and the complex, time-consuming nature of some assays such as aggregation studies. Platelet function testing lacks guidelines, is poorly standardized between laboratories, and rarely is evaluated by internal and external quality assurance exercises. The sensitivity, specificity, and diagnostic utility of some newer, simplified assays of platelet function have been evaluated in a range of clinical settings but corresponding quality assurance data for many established as well as emerging platelet function assays are lacking. Quality assurance issues relevant to testing platelet function are reviewed in this article, with a focus on their application to established and to new and emerging tests.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".