Bibliographic record
Abstract
Purpose: Tunneled catheters as chronic dialysis access is a reality of chronic hemodialysis. Infection of the catheter is an unavoidable complication. We researched the possibility of using Na Citrate as capping because of its anticoagulant effects and bactericidal properties. Methods: Rate of infections per 1000 calendar days with heparin capping was recorded for an eight month period before using Na Citrate. Flows <300 ml/min via catheters were also recorded for an eight month period before Na Citrate use, using treatments per 1000 calendar days. Both these parameters were recorded for an eight month period following the initiation of Na Citrate 4% for all catheters. Results: Our infection rate using heparin was 2.2 infections per 1000 catheter days. Infection rate after use of Na citrate was 1.2 infections per 1000 calendar days. Catheter flows <300 were 5.6 per 1000 calendar days using heparin, and 9.2 per 1000 calendar days after use of Na Citrate. There were no reactions related to Na Citrate, and no symptomatic hypocalcemia. Conclusions: Na Citrate is effective at reducing the number of infections when used as a capping solution for tunneled catheters. Na Citrate–capped catheters had more frequent declines in QB compared with heparin capping. Because of the availability of anti‐thrombolytic agents to preserve catheter patency, Na Citrate makes a safe option as a capping agent for long‐term catheter capping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".