A comparison of six major platelet function tests to determine the prevalence of aspirin resistance in patients with stable coronary artery disease
Bibliographic record
Abstract
AIMS: We sought to compare the results obtained from six major platelet function tests in the assessment of the prevalence of aspirin resistance in patients with stable coronary artery disease. METHODS AND RESULTS: 201 patients with stable coronary artery disease receiving daily aspirin therapy (> or =80 mg) were recruited. Platelet aggregation was measured by: (i) light transmission aggregometry (LTA) after stimulation with 1.6 mM of arachidonic acid (AA), (ii) LTA after adenosine diphosphate (ADP) (5, 10, and 20 microM) stimulation, (iii) whole blood aggregometry, (iv) PFA-100, (v) VerifyNow Aspirin; urinary 11-dehydro-thromboxane B(2) concentrations were also measured. Eight patients (4%, 95% CI 0.01-0.07) were deemed resistant to aspirin by LTA and AA. The prevalence of aspirin resistance varied according to the assay used: 10.3-51.7% for LTA using ADP as the agonist, 18.0% for whole blood aggregometry, 59.5% for PFA-100, 6.7% for VerifyNow Aspirin, and finally, 22.9% by measuring urinary 11-dehydro-thromboxane B(2) concentrations. Results from these tests showed poor correlation and agreement between themselves. CONCLUSION: Platelet function tests are not equally effective in measuring aspirin's antiplatelet effect and correlate poorly amongst themselves. The clinical usefulness of the different assays to classify correctly patients as aspirin resistant remains undetermined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".