Platelet Function Measured Using a Whole Blood Aggregometer Can Predict Bleeding Events
Bibliographic record
Abstract
AIM: We hypothesized that excessive suppression of platelet function due to antiplatelet therapy can increase the incidence of bleeding complications. The aim of the present study was to find whether we could predict bleeding events by measuring platelet function. METHODS: We enrolled 743 subjects whose platelet function was measured using a whole blood aggregometer based on a screen filtration pressure method. Of these subjects, 551 (74.2%) were treated with some type of antiplatelet agent. The endpoints were bleeding or ischemic events requiring hospitalization or extension of hospital stay. We prospectively compared the platelet function of subjects with and without bleeding or ischemic events. RESULTS: During 556 ± 207 days of follow-up, 52 (7.0%) bleeding events and 20 (2.7%) ischemic events were observed. Kaplan-Meier analysis using the log-rank test revealed that an aggregation rate of < 20% induced by 8 µ M adenosine diphosphate (ADP) was significantly associated with a greater number of bleeding events (11.9% vs. 5.2%; p = 0.0007). Cox proportional hazards model showed that age > 75 years (hazard ratio [HR], 1.78; 95% confidence interval [CI], 1.03-3.10; p = 0.039), estimated glomerular filtration rate < 60 ml/min/1.73 m(2) (HR, 1.82; 95% CI, 1.06-3.18; p = 0.031) and aggregation rate < 20% induced by 8 µ M ADP (HR, 2.18; 95% CI, 1.24-3.80; p = 0.0071) were independent predictors of bleeding events. CONCLUSIONS: Low platelet function demonstrated using a whole blood aggregometer was an independent predictor of bleeding complications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".