Patient- and treatment-related determinants of convective volume in post-dilution haemodiafiltration in clinical practice
Bibliographic record
Abstract
BACKGROUND: Large convective volumes are recommended for online haemodiafiltration (HDF) to maximize solute removal. There has been little systematic evaluation of factors that determine convective volumes in routine clinical practice. METHODS: In the present study, potential patient- and treatment-related determinants of convective volume were analysed in 235 consecutive patients on post-dilution HDF using multivariable linear regression models. All patients (age 64 +/- 14 years; 61% male) participated in the ongoing CONvective TRAnsport STudy (CONTRAST). Additionally, differences in convective volumes between dialysers were evaluated. RESULTS: The mean convective volume was 19.4 +/- 4.0 L (+/-SD) per treatment, with a large variation between the participating centres (centre means ranging from 13.4 +/- 0.9 L to 24.5 +/- 0.12 L, +/- SE). The mean filtration fraction of the blood flow was 25.9 +/- 3.6. In the multivariable analysis, factors that were significantly related to convective volume were haematocrit [inversely, regression coefficient (B) = -1.4 +/- 0.4 L per 10%], serum albumin (positively, B = 1.0 +/- 0.4 L per 10 g/L), blood flow rate (positively, B = 0.4 +/- 0.04 L per 10 mL/min) and treatment time (positively, B = 5.1 +/- 0.4 L/h). In addition, significant differences between dialysers were observed, likely explained by different operational conditions. CONCLUSIONS: Apart from increasing the treatment time and blood flow rate, convective volumes could be optimized by increasing the filtration fraction in each individual, provided that transmembrane pressures are well within safe limits. The precise role of dialyser characteristics on maximal achievable convective volumes in clinical practice is a topic for further research.
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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 | 0.004 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".