Biofeedback dialysis for hypotension and hypervolemia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intradialytic hypotension (IDH) is associated with morbidity and mortality. We conducted a systematic review to determine whether biofeedback hemodialysis (HD) can improve IDH and other outcomes, compared with HD without biofeedback. METHODS: Data sources included the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE and ISI Web of Science. We included randomized trials that enrolled adult patients (>18 years) with IDH or extracellular fluid expansion and that used biofeedback to guide ultrafiltration and/or dialysate conductivity. Two authors assessed trial quality and independently extracted data in duplicate. We assessed heterogeneity using I(2). We applied the GRADE framework for rating the quality of evidence. RESULTS: We found two parallel-arm randomized controlled clinical trials and six randomized crossover trials meeting inclusion criteria. All trials were open-label and at least four were industry-sponsored. Studies were small (median n = 27). No study evaluated hospitalization and the evidence for effect on mortality was of very low quality. Three studies assessed quality of life (QoL); none demonstrated benefit or harm, and quality of evidence was very low. Biofeedback significantly reduced IDH (risk ratio 0.61, 95% confidence interval 0.44-0.86; I(2)= 0%). Quality of evidence for this outcome was low due to risk of bias and potential publication bias. CONCLUSIONS: Biofeedback dialysis significantly reduces the frequency of IDH. Large and well-designed randomized trials are needed to assess the effects on survival, hospitalization and QoL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".