The history and future of online hemodiafiltration and online solutions in North America
Bibliographic record
Abstract
PURPOSE OF REVIEW: Online hemodiafiltration (OL-HDF) is a type of outpatient intermittent dialysis therapy using purified online dialysis fluid sourced from the city water supply. OL-HDF has been widely practiced in Europe and Japan, and its clinical effects have been reported for prevention of dialysis amyloidosis, inflammation, and dialysis hypotension. RECENT FINDINGS: A randomized controlled trial of all-cause mortality in postdilution OL-HDF and high-flux hemodialysis groups with replacement fluid volumes >23 l/session (CONVINCE study) reported a lower risk of all-cause mortality with OL-HDF compared to conventional hemodialysis. Whereas USA had not previously adopted OL-HDF, in February 2024 Fresenius' 5008K received 510K FDA approval, Although efforts to purify dialysis water and systems using dialysis fluid for HDF, such as those from Aksys (2002) and Nephros (2012), had been made in the past in the USA, they did not gain widespread adoption. Neighboring Canada has been conducting OL-HDF using the Gambro AK200 (1999), Baxter Artis (2009), B. Braun Dialog+ (2010), B. Braun Dialog IQ (2021) and the Fresenius 5008 (2013), all of which have received Health Canada approval for OL-HDF. SUMMARY: OL-HDF's introduction to the USA represents both a challenge and an opportunity for patient care and the nephrology community. As a potentially superior treatment for ESRD patients, OL-HDF enables larger volumes of exchange, reduces costs by creating online solutions to replace expensive offline fluids, makes HDF therapy affordable for outpatient setting, and may improve survival and quality of life. However, significant barriers - ranging from regulatory and reimbursement hurdles to infrastructural inadequacies - must be addressed. Whether OL-HDF can finally emerge as a transformational renal replacement therapy after its entry to the US healthcare system remains to be determined.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".