Optimizing the management of hemodialysis catheter occlusion
Bibliographic record
Abstract
Hemodialysis catheter occlusion compromises hemodialysis adequacy and increases the cost of care. Repeated administration of alteplase in hemodialysis catheters typically produces only short-term benefits. The purpose of this study was to design, implement and evaluate the efficacy of a step-by-step algorithm to optimize the management of hemodialysis catheter occlusion. The study had a prospective quasi-experimental design in two parts. Baseline data on the use of alteplase and catheter exchange were collected during Part I; while, Part II consisted of algorithm implementation. Rates of alteplase use and catheter exchange per 1000 catheter days were main outcomes of the study. One-hundred and seventy-two catheters in 131 patients were followed up during the course of the study. The vast majority of the study population were on clopidogrel or aspirin (75%); whereas, approximately 11% were on warfarin. The adjusted rate of alteplase use was not significantly different after algorithm implementation (Part I vs. Part II relative risk: 1.10; 95% CI: 0.73 – 1.65, p > 0.05). Similarly, catheter exchange rates were not significantly different in both parts of the study (1.12 vs. 1.03 per 1000 catheter-days, p > 0.05). Regression analysis showed that the rate of alteplase use was inversely related to the catheter age (p < 0.05). In a secondary analysis on a subgroup of patients with occlusion-related catheter exchanges (n = 28), the number of alteplase administrations significantly increased with longer waiting time for catheter exchange (p < 0.05). In conclusion the hemodialysis catheter management algorithm was not effective in decreasing the rate of alteplase use.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".