Screening and diagnosis of inherited platelet disorders
Bibliographic record
Abstract
Inherited platelet disorders are important conditions that often manifest with bleeding. These disorders have heterogeneous underlying pathologies. Some are syndromic disorders with non-blood phenotypic features, and others are associated with an increased predisposition to developing myelodysplasia and leukemia. Platelet disorders can present with thrombocytopenia, defects in platelet function, or both. As the underlying pathogenesis of inherited thrombocytopenias and platelet function disorders are quite diverse, their evaluation requires a thorough clinical assessment and specialized diagnostic tests, that often challenge diagnostic laboratories. At present, many of the commonly encountered, non-syndromic platelet disorders do not have a defined molecular cause. Nonetheless, significant progress has been made over the past few decades to improve the diagnostic evaluation of inherited platelet disorders, from the assessment of the bleeding history to improved standardization of light transmission aggregometry, which remains a "gold standard" test of platelet function. Some platelet disorder test findings are highly predictive of a bleeding disorder and some show association to symptoms of prolonged bleeding, surgical bleeding, and wound healing problems. Multiple assays can be required to diagnose common and rare platelet disorders, each requiring control of preanalytical, analytical, and post-analytical variables. The laboratory investigations of platelet disorders include evaluations of platelet counts, size, and morphology by light microscopy; assessments for aggregation defects; tests for dense granule deficiency; analyses of granule constituents and their release; platelet protein analysis by immunofluorescent staining or flow cytometry; tests of platelet procoagulant function; evaluations of platelet ultrastructure; high-throughput sequencing and other molecular diagnostic tests. The focus of this article is to review current methods for the diagnostic assessment of platelet function, with a focus on contemporary, best diagnostic laboratory practices, and relationships between clinical and laboratory findings.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".