Current Canadian Approaches to Dialysis for Acute Renal Failure in the ICU
Bibliographic record
Abstract
BACKGROUND: Although there is a very high mortality rate (>50%) with acute renal failure (ARF) in the intensive care unit (ICU), there is no general consensus on the best dialysis treatment for this condition. METHODS: We surveyed by mail questionnaire, all adult academic and community registered Canadian nephrology centers that offer treatment for ARF. RESULTS: The overall response rate was 59% (53/90). Comparing current dialysis methods with those utilized 5 years ago, the largest increase was in continuous renal replacement therapies (CRRT) (26 vs. 9%). Both intermittent hemodialysis (IHD) and peritoneal dialysis decreased in utilization. The predominant current CRRT methods utilized venovenous access (80%), as compared to 5 years ago when arteriovenous was the most common (52%). Despite data from chronic dialysis (and preliminary data in ARF) suggesting reduced mortality and morbidity with increasing dialysis dose, there was no formal method of dialysis prescription monitoring in over 75% of the centers. CONCLUSION: Notwithstanding a lack of definitive evidence of superior outcomes with CRRT compared to older methods, the utilization of CRRT is dramatically increasing for the treatment of ARF in Canada. Whether this shift towards CRRT, and whether more attention to dialysis dose in ARF, might be expected to lead to better outcomes, requires further evaluation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".