Hemodialysis patients receiving a greater Kt dose than recommended have reduced mortality and hospitalization risk
Bibliographic record
Abstract
Achieving an adequate dialysis dose is one of the key goals for dialysis treatments. Here we assessed whether patients receiving the current cleared plasma volume (Kt), individualized for body surface area per recommendations, had improved survival and reduced hospitalizations at 2 years of follow-up. Additionally, we assessed whether patients receiving a greater dose gained more benefit. This prospective, observational, multicenter study included 6129 patients in 65 Fresenius Medical Care Spanish facilities. Patients were classified monthly into 1 of 10 risk groups based on the difference between achieved and target Kt. Patient groups with a more negative relationship were significantly older with a higher percentage of diabetes mellitus and catheter access. Treatment dialysis time, effective blood flow, and percentage of on-line hemodiafiltration were significantly higher in groups with a higher dose. The mortality risk profile showed a progressive increase when achieved minus target Kt became more negative but was significantly lower in the group with 1 to 3 L clearance above target Kt and in groups with greater increases above target Kt. Additionally, hospitalization risk appeared significantly reduced in groups receiving 9 L or more above the minimum target. Thus, prescribing an additional 3 L or more above the minimum Kt dose could potentially reduce mortality risk, and 9 L or more reduce hospitalization risk. As such, future prospective studies are required to confirm these dose effect findings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".