Characterization of multiple platelet activation pathways in patients with bleeding as a high-throughput screening option: use of 96-well Optimul assay
Bibliographic record
Abstract
Up to 1% of the population have mild bleeding disorders, but these remain poorly characterized, particularly with regard to the roles of platelets. We have compared the usefulness of Optimul, a 96-well plate-based assay of 7 distinct pathways of platelet activation to characterize inherited platelet defects in comparison with light transmission aggregometry (LTA). Using Optimul and LTA, concentration-response curves were generated for arachidonic acid, ADP, collagen, epinephrine, Thrombin receptor activating-peptide, U46619, and ristocetin in samples from (1) healthy volunteers (n = 50), (2) healthy volunteers treated with antiplatelet agents in vitro (n = 10), and (3) patients with bleeding of unknown origin (n = 65). The assays gave concordant results in 82% of cases (κ = 0.62, P < .0001). Normal platelet function results were particularly predictive (sensitivity, 94%; negative predictive value, 91%), whereas a positive result was not always substantiated by LTA (specificity, 67%; positive predictive value, 77%). The Optimul assay was significantly more sensitive at characterizing defects in the thromboxane pathway, which presented with normal responses with LTA. The Optimul assay is sensitive to mild platelet defects, could be used as a rapid screening assay in patients presenting with bleeding symptoms, and detects changes in platelet function more readily than LTA. This trial was registered at www.isrctn.org as #ISRCTN 77951167.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".