Relationship between Blood Flow in Central Venous Catheters and Hemodialysis Adequacy
Bibliographic record
Abstract
Central venous catheter dysfunction is a frequent problem and often is defined as a blood flow <300 ml/min. This prospective, cross-sectional study included 259 patients and examined the relationship between catheter blood flow and dialysis adequacy as measured by urea reduction ratio (URR), single pool urea kinetics, and online effective ionic dialysance clearance. Dialysis adequacy at blood flow rates of <300, <275, and <250 ml/min; sensitivity; specificity; and positive and negative predictive values were calculated. Mean blood flow was 352 ml/min (SD +/- 48.8). Mean blood flow <300 ml/min occurred in 10.5% of the patients, and only 26% had a URR of <65%. Maximum blood flows <300 ml/min occurred in 6.9% of patients, and only 22.2% had URR <65%. The positive predictive value of mean blood flow of <300 and <275 ml/min to predict a URR <65% was 22 and 40%, respectively. Using receiver operator characteristic curves, the area under the curve was not significantly different for blood flows of 300, 275, or 250 ml/min. This study indicates that mean blood flows <300 ml/min are not commonly associated with dialysis inadequacy. Setting a single blood flow cut point of <300 ml/min to define the need for intervention will result in a significant number of unnecessary interventions. There is a need to reexamine the definition of catheter dysfunction and expand the definition beyond blood flow rates.
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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 | 0.010 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".