Comparison of two methods to assess variability of platelet response to anti-platelet therapies in patients with acute coronary syndrome undergoing angioplasty
Bibliographic record
Abstract
The study investigated the clinical usefulness of a new method to evaluate platelet activation and the variability of platelet response to anti-platelet therapy in patients undergoing percutaneous transluminal coronary angioplasty (PTCA). Platelet activation was assessed in parallel by a new method for platelet density measurements (MPC, Mean Platelet Component Concentration), on the automated ADVIA 120 Hematology System and by the classic measurement of P-selectin (CD62P) expression, on a fluorescence flow cytometer. Patients received a loading dose of clopidogrel (300 mg; n = 29) or a bolus of abciximab (0.25 mg/kg; n = 15). Blood samples were collected before (baseline) and at different times after PTCA and antiplatelet drugs administration. Our data showed a close inverse correlation between the change in MPC and the CD62P fluorescence surface marker expression (r = -0.776, P<0.0001). Individual platelet activation determinations in patients receiving either clopidogrel or abciximab showed a variation in platelet activation as assayed by MPC and CD62P expression. Patients were characterized as having either high platelet activity upon admission and positive response to treatment or no detectable platelet activation before or after treatment. This study demonstrates the heterogeneity of platelet activation states in ACS patients undergoing coronary angioplasty. The present work also illustrates the potential use of the MPC parameter, generated on an automated hematology system, to define high risk patients and to monitor the variability of platelet response to anti-platelet therapies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".