Meta-Analysis of Randomized Controlled Trials Using Tool-Assisted Target Weight Adjustments in Chronic Dialysis Patients
Bibliographic record
Abstract
Introduction Technological adjuncts have been developed to improve the accuracy of fluid removal goals in maintenance dialysis recipients. We aimed to determine whether the introduction of these tools has been shown to impact clinical outcomes. Methods We performed a systematic review and meta-analysis of randomized controlled trials that compared fluid management guided by technological adjuncts to standard care in hemodialysis and peritoneal dialysis. The primary outcome was all-cause mortality. Secondary outcomes were cardiovascular events, hospitalizations, intradialytic hypotension, blood pressure, symptoms, antihypertensive medications. and left ventricular mass index. Results Of the 2940 citations retrieved, we identified a total of 12 eligible trials comprising 2406 participants. In the 10 studies ( n = 2111) with data on mortality, the use of adjunct technologies was not associated with a reduction of mortality (rate ratio [RR]: 0.92; confidence interval [CI]: 0.57–1.51; I 2 = 36%). The intervention conferred a reduction in systolic arterial pressure (mean difference: −3.14; CI: −5.89 to −0.38; I 2 = 39%) but did not affect other outcomes. In a subgroup analysis, bioimpedance was associated with a reduced risk of hospitalization (RR: 0.68; CI: 0.46–0.99; I 2 = 55%). The risk of bias was high or unclear in most studies and the quality of evidence was judged to be low. Conclusions Among maintenance dialysis recipients, technological adjuncts for fluid management did not improve survival. Trials mostly investigated the use of bioimpedance, whereas the evidence for use of other technologies remain very scarce. Future adequately powered trials should assess a broader array of promising technologies using meaningful clinical outcomes over a prolonged follow-up duration.
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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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".