Acute thrombocytopenia associated with eptifibatide therapy.
Bibliographic record
Abstract
BACKGROUND: Platelet glycoprotein (GP) IIb/IIIa receptor blockade improves clinical outcomes in percutaneous coronary interventions and in acute coronary syndromes. Thrombocytopenia is a serious complication well described with the use of the prototype GPIIb/IIIa inhibitor abciximab. Its association with other agents of this class has been underemphasized. OBJECTIVES: To determine the incidence of thrombocytopenia in a cohort of patients treated with eptifibatide at a tertiary cardiac centre. PATIENTS AND METHODS: Chart review of consecutive patients treated with eptifibatide at the study institution. RESULTS: There were four (1.3%) cases of acute thrombocytopenia (platelet count less than 100 x 10(9)/L) among 305 patients reviewed. One patient had been previously exposed to eptifibatide. The other three patients are described. In each case, platelet counts declined within 6 h of receiving eptifibatide. Recovery of platelet counts was noted within 6 to 30 h after withdrawal of eptifibatide. No patient suffered an adverse clinical event related to thrombocytopenia. CONCLUSIONS: It is important to monitor platelet counts closely after initiation of GPIIb/IIIa inhibitor therapy, not only for abciximab, but also for small molecule inhibitors such as eptifibatide. Monitoring of platelet counts at 2 to 6 h and 24 h will detect most cases of acute thrombocytopenia. Adverse events may be prevented by prompt discontinuation of GPIIb/IIIa inhibitor therapy.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".