Impact of Aspirin and Clopidogrel Interruption on Platelet Function in Patients Undergoing Major Vascular Surgery
Bibliographic record
Abstract
AIMS: To investigate functional platelet recovery after preoperative withdrawal of aspirin and clopidogrel and platelet function 5 days after treatment resumption. METHODS/RESULTS: We conducted an observational study, which prospectively included consecutive patients taking aspirin, taking clopidogrel, and untreated controls (15 patients in each group). The antiplatelet drugs were withdrawn five days before surgery (baseline) and were reintroduced two days after surgery. Platelet function was evaluated by optical aggregation in the presence of collagen, arachidonic acid (aspirin) and ADP (clopidogrel) and by VASP assay (clopidogrel). Platelet-leukocyte complex (PLC) level was quantified at each time-point. At baseline, platelet function was efficiently inhibited by aspirin and had recovered fully in most patients 5 days after drug withdrawal. PLC levels five days after aspirin reintroduction were similar to baseline (+4±10%; p = 0.16), in line with an effective platelet inhibition. Chronic clopidogrel treatment was associated with variable platelet inhibition and its withdrawal led to variable functional recovery. PLC levels were significantly increased five days after clopidogrel reintroduction (+10±15%; p = 0.02), compared to baseline. CONCLUSIONS: Aspirin withdrawal 5 days before high-bleeding-risk procedures was associated with functional platelet recovery, and its reintroduction two days after surgery restored antiplaletet efficacy five days later. This was not the case of clopidogrel, and further work is therefore needed to define its optimal perioperative management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".