Evaluation of participants with suspected heritable platelet function disorders including recommendation and validation of a streamlined agonist panel
Bibliographic record
Abstract
Light transmission aggregometry (LTA) is used worldwide for the investigation of heritable platelet function disorders (PFDs), but interpretation of results is complicated by the feedback effects of ADP and thromboxane A(2) (TxA(2)) and by the overlap with the response of healthy volunteers. Over 5 years, we have performed lumi-aggregometry on 9 platelet agonists in 111 unrelated research participants with suspected PFDs and in 70 healthy volunteers. Abnormal LTA or ATP secretion test results were identified in 58% of participants. In 84% of these, the patterns of response were consistent with defects in Gi receptor signaling, the TxA(2) pathway, and dense granule secretion. Participants with defects in signaling to Gq-coupled receptor agonists and to collagen were also identified. Targeted genotyping identified 3 participants with function-disrupting mutations in the P2Y(12) ADP and TxA(2) receptors. The results of the present study illustrate that detailed phenotypic analysis using LTA and ATP secretion is a powerful tool for the diagnosis of PFDs. Our data also enable subdivision at the level of platelet-signaling pathways and in some cases to individual receptors. We further demonstrate that most PFDs can be reliably diagnosed using a streamlined panel of key platelet agonists and specified concentrations suitable for testing in most clinical diagnostic laboratories.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".