Electron microscopy examination of platelet whole mount preparations to quantitate platelet dense granule numbers: Implications for diagnosing suspected platelet function disorders due to dense granule deficiency
Bibliographic record
Abstract
INTRODUCTION: Dense granule (DG) deficiency (DGD) is a feature of some platelet function disorders (PFD) with a prevalence similar to von Willebrand disease. Most laboratories assess for DGD using whole mount platelet preparations and electron microscopy (EM). We evaluated our experiences with this test and associations between DGD and bleeding. METHODS: Dense granule EM records for 2006-2017 were examined for patients and simultaneously tested controls, and for an overlapping PFD study cohort to evaluate findings and their relationship to bleeding. RESULTS: More patient than control samples had reduced DG counts (6.5% vs 0.3%, P < .01). DG counts showed no relationship to age or mean platelet volume and had acceptable within-subject variability that was higher for DGD than control participants (28% vs 12%). Repeat tests confirmed DGD in all persons with initial DG counts <4.0/platelet, but not in those with less severe reductions (4.0-4.8 DG/platelet) or normal DG counts (≥4.9 DG/platelet). Aggregometry and adenosine triphosphate release tests, respectively, had only ~52% and 70% sensitivity for DGD. Confirmed DGD by EM was associated with higher bleeding scores and a bleeding disorder. CONCLUSION: Whole mount EM is useful for the evaluation of suspected PFD due to DGD and detects abnormalities associated with bleeding.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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 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".