Antiplatelet Medications in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patients with end stage renal disease (ESRD) are often prescribed antiplatelet medications. However, these patients are also at increased risk of bleeding compared with the general population, and an aim was made to quantify this risk with antiplatelet agents. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A systematic review of the literature (Medline, EMBASE, Cochrane CENTRAL and Google Scholar databases) was done to determine the bleeding risk in ESRD patients prescribed antiplatelet therapy. The secondary outcome was the effect on access thrombosis. All case series, cohort studies and clinical trials were considered if they included ten or more ESRD patients, assessed bleeding risk with antiplatelet agents, and lasted for more than 3 mo. RESULTS: Sixteen studies, including 40,676 patients, were identified that met predefined inclusion criteria. Due to study heterogeneity and weaknesses in methodology, bleeding rates were not pooled across studies. However, the bleeding risk appears to be increased for hemodialysis patients treated with combination antiplatelet therapy. The results are mixed for studies using a single antiplatelet agent. Antiplatelet agents appear to be effective in preventing shunt and central venous catheter thrombosis, but not for preventing thrombosis of arteriovenous grafts. CONCLUSION: The risks and benefits of antiplatelet agents in ESRD patients remain poorly defined. Until a clinical trial addresses this in the dialysis population, individual risk stratification taking into account the increased risk of bleeding should be considered before initiating antiplatelet agents, especially in combination 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".