Results of an External Proficiency Testing Exercise on Platelet Dense-Granule Deficiency Testing by Whole Mount Electron Microscopy
Bibliographic record
Abstract
Performance on specialized diagnostic tests for platelet disorders, including dense-granule deficiency, is rarely evaluated by external quality assessment (EQA). Members of the North American Specialized Coagulation Laboratory Association that evaluate platelet dense-granule deficiency commonly use whole-mount electron microscopy (EM) methods. This observation led us to develop a pilot EQA survey with standardized EM images and clinical samples on grids from a healthy control subject and a subject with dense-granule deficiency. The survey participants were 8 centers, including 2 with no experience in platelet whole mount EM. All participants, including inexperienced sites, correctly interpreted findings for the normal and dense-granule-deficient platelets. Among experienced sites, agreement was excellent (>82%) on platelet structures to count or not count as dense granules. Participants indicated that future EQA challenges should include clinical samples on grids and standardized images. This is the first report that platelet EM can be assessed by EQA.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".