External Quality Assessment of Platelet Disorder Investigations: Results of International Surveys on Diagnostic Tests for Dense Granule Deficiency and Platelet Aggregometry Interpretation
Bibliographic record
Abstract
The quality of platelet aggregation and dense granule deficiency testing is important for diagnosing platelet function disorders. After a successful pilot exercise on diagnosing platelet dense granule deficiency by electron microscopy (EM), the North American Specialized Coagulation Laboratory Association (NASCOLA) has launched regular external quality assurance (EQA) for dense granule EM, as well as for the interpretation of platelet aggregation findings. EQA records were analyzed to assess performance. For EM EQA, between 2009 and 2011, there was excellent performance in distinguishing normal from dense granule-deficient samples and good (>70%) agreement on classifying most electron dense structures in platelets. For aggregation EQA, some normal variants were misclassified and overall case interpretations were more acceptable for rare disorders than for common findings. NASCOLA experiences with these EQAs indicate that there is a need to improve the quality of platelet disorder evaluations. For aggregometry interpretations, deficits in performance could be addressed by translating guideline recommendations into practice.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".