Risk factors for increased variability in dialysis delivery in haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Numerous events may occur during a haemodialysis session, leading to variation in the quantity of dialysis received. The purpose of this study was to identify risk factors for variability in haemodialysis delivery. METHODS: Variability in dialysis delivery was expressed by the coefficient of variation (CV%) and calculated for the volume of blood processed (VBP) for all treatments and the monthly urea reduction ratio (URR) in each patient over an 8 month period. The univariate and multivariate relationships between various predictor variables and the URR and VBP CV% were determined. RESULTS: Eighty-nine patients were identified who met study criteria. The mean VBP and URR CV% were 10.3 +/- 4.7 and 5.4 +/- 3.8%, respectively. Patients with tunnelled catheters and total nursing-care patients had higher VBP and URR CV%, as evaluated by multivariate analysis. Patients with inadequate dialysis (mean URR <65%) had a higher VBP CV% than those patients with mean URR values > or =65% (14.8 +/- 5.4 vs 9.7 +/- 4.5%; P = 0.01). An accurate determination of the URR in 90% of patients required 14 measurements in patients with catheters vs three and two measurements in arteriovenous fistulae and polytetrafluoroethylene grafts, respectively. CONCLUSIONS: This study demonstrated that the use of a venous tunnelled catheter and dialysis in a total nursing-care unit were the only factors independently associated with greater variability in both VBP and URR. Attention to individual dialysis sessions in patients with tunnelled catheters or in a total nursing-care unit is prudent, particularly when identifying reasons for under-dialysis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".