Elevated Removal of Middle Molecules without Significant Albumin Loss with Mixed-Dilution Hemodiafiltration for Patients Unable to Provide Sufficient Blood Flow Rates
Bibliographic record
Abstract
BACKGROUND: We examined the hypothesis that mixed-dilution online hemodiafiltration (MIXED) rather than predilution online hemodiafiltration (PRE) could enable patients with low blood flow rate (Qb) to benefit from advantages of convective therapies. METHODS: Thirty-eight patients were included in a prospective, randomized, crossover and multicenter study conducted with a view to comparing the equilibrated Kt/V, reduction ratio (RR) of phosphates, β2-microglobulin (β2-M) and myoglobin (myo) between PRE and MIXED, each at two Qb values of 250 and 300 ml/min during 4 h sessions with a FX1000HDF dialyzer. Albumin losses (Alb) were also measured in 12 patients. RESULTS: MIXED was always found to be more efficient compared to PRE notably for middle molecules (MM). RRβ2-M: MIX250: 81.3 ± 3.6 vs. PRE250: 75.2 ± 5.9; MIX300: 82.7 ± 3.6 vs. PRE300: 78.1 ± 5.4; RRmyo: MIX250: 70.2 ± 3.6 vs. PRE250: 42.6 ± 2.6; MIX300: 70.6 ± 3.6 vs. PRE300: 45.7 ± 3.6 and with Alb <3.0 g/session. CONCLUSION: MIXED allows patients unable to provide sufficiently high Qb to achieve high levels of MM removal.
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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 | 0.001 |
| 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 | 0.000 | 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".