Effects of hemodialysis access surveillance on reducing risk of hemodialysis access thrombosis: A meta‐analysis of randomized studies
Bibliographic record
Abstract
INTRODUCTION: Vascular access thrombosis remains the Achilles Heel for many a hemodialysis patient. We performed a systematic review and meta-analysis to assess the impact of monitoring vascular access blood flow on prediction and prevention of vascular access thrombosis. We hypothesized that monitoring vascular access blood flow has a pivotal role in lowering the risk of thrombosis and subsequent access failure. METHODOLOGY: We conducted a systematic review in PubMed and EMBASE databases to identify randomized studies that have assessed the effect of hemodialysis access surveillance on the risk of thrombosis. A random-effects model was used for the meta-analysis. Subgroup analysis was performed among patients with arterio-venous fistula (AV-fistulas) and those with arterio-venous graft (AV-grafts). RESULTS: Ten randomized studies were included in the meta-analysis. The total number of patients included in the analysis was 1430. On performing the random-effects model among the included studies, hemodialysis access surveillance was associated with better outcomes (risk ratio = 0.73, 95% confidence interval ranges from 0.55 to 0.98). The analysis of the AV-fistula group showed an estimated overall risk ratio of 0.55 (95% confidence interval ranges from 0.33 to 0.89) favoring access surveillance. However, in the AV-grafts group, the estimated overall risk ratio was 0.92 (95% confidence interval ranges from 0.65 to 1.29) showing no additional benefit for access surveillance. CONCLUSION: Hemodialysis access surveillance using access blood flow monitoring can reduce the risk of access thrombosis for patients with AV-fistulas, but this is not the case with AV-grafts.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".