Inherited disorders of platelet function and challenges to diagnosis of mucocutaneous bleeding
Bibliographic record
Abstract
SUMMARY: Platelets play a pivotal role in the arrest of bleeding at sites of vascular injury. Following endothelial damage, they respond rapidly by adhesion to subendothelial matrix proteins resulting in platelet activation, spreading, aggregation, secretion and recruitment of additional platelets to form the primary haemostatic plug. This mass provides a surface for thrombin generation and fibrin mesh formation that stabilizes the clot. Careful study of patients with inherited platelet disorders and, subsequently, of informative animal models, has identified structural platelet abnormalities that have enhanced our understanding of platelet function. The investigations of rare, but severe, inherited platelet disorders have led us to the discovery of causative molecular defects. One of the most informative is the rare autosomal recessive disorder Glanzmann thrombasthenia, caused by defect or deficiency in the platelet integrin alphaIIbbeta3, resulting in absent platelet aggregation and a significant clinical bleeding diathesis. Our new challenge is to understand the mechanisms underlying more common, but less well-defined, mucocutaneous bleeding (MCB) disorders. Present diagnostic testing for platelet function disorders and von Willebrand's Disease often fails to identify the cause of bleeding in individuals with inherited MCB.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".