On-line Haemodiafiltration in the Management of Acute Renal Failure
Bibliographic record
Abstract
Purpose – Patients with acute renal failure (ARF) offer particular challenges in terms of cardiovascular stability during renal replacement therapy. Targets for small solute clearance are unclear, removal of inflammatory mediators is desirable, and biocompatible membranes are essential. This study examined the impact of using on-line haemodiafiltration (HDF), which combines convective and diffusive transport, with replacement fluid being produced “on-line” from reverse-osmosed water. Methods – Clinical outcomes were examined in all patients presenting with ARF. Initially, management of ARF included daily haemodialysis (4 hours with biocompatible membrane). Subsequently, all patients were managed with on-line HDF using the Gambro AK200 Ultra™, daily for 4 hours. In the initial stages of patient management, CVVH was used in the ICU setting throughout the study period. Pre 1 Pre 2 Post 1 Post 2 Alive (total) 4 9 18 29* Dead 10 5 9 11* Alive on chronic HD 5 8 11 3* Results – A comparison was made between the years prior to (Pre-1 and 2) and following (Post-1 and 2) the change to on-line HDF. (* p < 0.001 vs Pre 1/Pre 2). Conclusions – On-line HDF is a modality that combines cardiovascular stability and significant middle molecule clearance. Using on-line HDF in the management of ARF, there were significant improvements in survival and recovery of renal function.
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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 | 0.001 |
| 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".