Update on diagnostic testing for platelet function disorders: What is practical and useful?
Bibliographic record
Abstract
INTRODUCTION: Platelet function disorders (PFD) are an important group of bleeding disorders that require validated and practical laboratory strategies for diagnosis. METHODS: This review summarizes the authors' experiences, current literature, and an international survey to evaluate the practices of diagnostic laboratories that offer tests for PFD. RESULTS: Blood counts, blood film review, and aggregation tests are the most commonly performed investigations for PFD and help determine whether there is thrombocytopenia and/or defective platelet function due to a variety of causes. The performance characteristics of tests for PFD, and the level of evidence that these tests detect bleeding problems, are important issues to determine where tests are useful for diagnostic or correlative purposes, or research only uses. Platelet aggregation assays, and quantitative analysis of platelet dense granule numbers, are tests with good performance characteristics that detect abnormalities associated with increased bleeding in a significant proportion of individuals referred for PFD investigations. Lumiaggregometry estimates of platelet adenosine triphosphate release show greater variability which limits the diagnostic usefulness. Diagnostic laboratories report that fiscal and other constraints, including a lack of high-quality evidence, limit their ability to offer an expanded test menu for PFD. CONCLUSION: PFD are clinically important bleeding disorders that remain challenging for diagnostic laboratories to investigate. While some PFD tests are well validated for diagnostic purposes, gaps in scientific evidence and resource limitations influence diagnostic laboratory decisions on which PFD tests to offer.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".