Current Strategies in Diagnosis of Inherited Storage Pool Defects
Bibliographic record
Abstract
Inherited platelet defects lead to bleeding symptoms of varying severity. Typically, easy bruising, petechiae, epistaxis, and mucocutaneous bleeding are observed in affected patients. The platelet defects are classified into disorders affecting either platelet surface receptors or intracellular organelles of platelets. The latter are represented by platelet storage pool diseases (SPD) which share a defect of platelet granules. Platelet α-granules, δ-granules, or both may be affected resulting in the clinical picture of α-SPD (e.g. Gray platelet syndrome, Quebec platelet disorder, arthrogryposis, renal dysfunction, and cholestasis syndrome), δ-SPD (e.g. Hermansky-Pudlak syndrome, Chediak-Higashi syndrome, Griscelli syndrome), or αδ-SPD (e.g. X-linked thrombocytopenia, Wiskott-Aldrich syndrome). Diagnosis of SPD is very extensive and requires platelet aggregation and flow cytometry analyses with interpretation from a specialist. Many of these disorders share common treatments, however, efficacy can vary between different patients. Therapy regiments with tranexamic acid, DDAVP, activated FVIIa, and platelet transfusions have been published. Stem cell or bone marrow transplantations are preserved for severe defects. Here, we describe the pathophysiology, clinical manifestations, and diagnosis of the major human SPDs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".