When it's not Glanzmann thrombasthenia or Bernard-Soulier syndrome: diagnosing other qualitative platelet disorders
Bibliographic record
Abstract
Inherited and acquired disorders that qualitatively impair platelet function represent important and commonly encountered conditions. While some rare, well-characterized conditions (eg, Glanzmann thrombasthenia and Bernard-Soulier syndrome) have pathognomonic findings and glycoprotein deficiencies, the more commonly encountered platelet function disorders are much more heterogeneous and challenging to diagnose. Qualitative platelet disorders typically present with mild to moderate, mucocutaneous, and challenge-related bleeding, particularly for hemostatic challenges prior to diagnosis. Diagnostic tests should assess platelet counts and size; platelet morphology by light microscopy; platelet function in aggregation assays (a "gold standard" test for qualitative platelet disorders); platelet-dense granule numbers, granule contents, and/or release; and, less commonly, platelet glycoproteins, procoagulant function, α-granule release, or ultrastructure. Genetic tests can be helpful, but the chances of finding a diagnostic, disease-causing, pathogenic mutation with a platelet disorder genetic test panel is much lower if there is not an a priori suspected cause and/or inherited thrombocytopenia. Commonly, the diagnosis of a qualitative platelet disorder is made after confirming impaired platelet aggregation responses to multiple agonists, with a pattern that excludes rare disorders (eg, Glanzmann thrombasthenia and Bernard-Soulier syndrome), and/or platelet-dense granule deficiency; both findings are highly predictive of a bleeding disorder. To communicate and discuss diagnostic test findings with patients and optimize care, it is also important to have knowledge of the bleeding risks for qualitative platelet disorders and their typical responses to therapies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".