The effect of extended‐hours hemodialysis on outcomes: A systematic review and meta‐analysis
Bibliographic record
Abstract
Extended-hours hemodialysis is associated with improvements in quality of life (QoL) and mortality, but it may accelerate the loss of residual kidney function (RKF) and increase vascular access complications. Multiple established databases were systematically searched; randomized and non-randomized studies were pooled separately. QoL outcomes were assessed using standardized mean difference (SMD), vascular access adverse events and mortality were assessed with relative risk ratios (RR). Four hundred seventy-six patients from six trials were eligible. Data from randomized controlled trials (RCTs) could only be synthesized for vascular access adverse events and mortality, which demonstrated no significant change in vascular access adverse events (RR 1.25, 95% CI 0.88 to 1.77) or mortality (RR 2.29, 95% CI 0.60 to 8.71). Pooled data from non-randomized trials demonstrated no significant difference in QoL (SF-36 Physical Component Summary SMD 0.61, 95% CI -0.10 to 1.31, SF-36 Mental Component Summary SMD -0.04, 95% CI -0.61 to 0.54). RKF was assessed in one report which demonstrated a potential reduction over 12 months with extended-hours hemodialysis. The majority of trials had high risk of bias. Extended-hours hemodialysis was not associated with improved QoL or mortality, or increased vascular access events. Adequately powered RCTs are needed to fully assess extended-hours 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".