Current Strategies in Diagnosis of Inherited Storage Pool Defects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it