Online hemodiafiltration: 4 years of clinical experience
Bibliographic record
Abstract
Online hemodiafiltration (online HDF) is a new hemodialysis technique combining convection and diffusion and thus also enabling the purification of large molecules. As yet, only a small number of clinical experiences have been published about the effectiveness and safety of online HDF. We present a prospective and observational study conducted on 31 patients treated with online HDF in our center in the last 4 years. The purpose of the study is to compare the evolution of the following aspects before and after starting online HDF: dose of dialysis, purification of medium-sized/large molecules, inflammation, nutrition, Ca-P metabolism, anemia, and intradialytic complications. Online HDF increased Kt/V to 31.0% (p > 0.001) and reduced postdialysis beta(2)-M to 66.4% (p > 0.001). The rest of the parameters analyzed did not vary significantly. During online HDF, episodes of symptomatic hypotension fell by 45% in relation to conventional hemodialysis, and no relevant complication occurred. Online HDF is very useful in patients in whom we need to increase replacement therapy, such as patients with a large body surface, those in whom we suspect a residual syndrome or those who have been receiving dialysis for a long time and for whom we wish to prevent amyloidosis. Online HDF is safe and better tolerated than conventional hemodialysis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".