Strategies to Prevent Hemodialysis Catheter Dysfunction
Bibliographic record
Abstract
Millions of patients with kidney failure rely on hemodialysis central venous catheters (CVCs) for their life-sustaining dialysis treatments. CVC dysfunction necessitates removal of up to 20% of CVCs and is an important problem for patients with kidney failure. Thrombosis and fibrin sheath formation are the most common mechanisms of CVC dysfunction beyond the first week after insertion. Factors such as female sex, left-sided CVC placement, and prior CVC dysfunction are associated with a higher risk of dysfunction. Patient-specific factors contribute substantially to variation in the number of CVC dysfunction events. Weekly thrombolytic locks have been shown to improve CVC blood flow rates, prevent infection, and reduce dysfunction requiring removal. However, routine administration may not be cost-effective in hemodialysis units with low infection rates, and targeted use among patients with established CVC dysfunction has not been studied. Concentrated heparin lock ( e.g ., 5000 versus 1000 international unit/ml) has been associated with lower requirements for therapeutic CVC thrombolysis but greater systemic bleeding risks and costs. Citrate 4% was noninferior to standard heparin locks to prevent thrombosis, may cause less bleeding, and is less costly in some countries. Tunneled CVCs with a symmetrical tip have been associated with a lower risk of CVC dysfunction compared with those with a step tip. Multifaceted CVC care interventions can reduce the incidence of dysfunctional CVCs by 33% compared with usual care. Future research to identify patients at high risk of CVC dysfunction will inform individualized vascular access plans, targeted use of preventive strategies, and enrollment criteria for future clinical trials.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".