A meta‐analysis of sodium profiling techniques and the impact on intradialytic hypotension
Bibliographic record
Abstract
Introduction Hemodialysis has improved in recent years, however, despite such improvements, intra-dialytic hypotensive episodes still persist which can lead to a reduction in the overall effectiveness of the treatment. Profiling sodium levels during dialysis can improve vascular refilling and therefore may prevent hypotensive events. A number of profiling methods exist and this meta-analysis set out to examine the effectiveness of these methods. Methods To assess the effectiveness of hemodialysis sodium profiling techniques. A review and meta-analysis analytical framework was used. A search was conducted using Medline, Embase and CINAHL, Scopus and Web of Knowledge between 1946 and 2014 of published English-language peer reviewed randomized control studies. In total 10 articles were retrieved and included in the review. All data was abstracted with a standardized data collection form. Stata 11.2 (Stata Corp) was used to analyse the data. Actual numbers of hypotensive events were pooled between studies. Analysis of subgroups was performed on sodium profile type. The data were further investigated using meta-regression. Publication bias was also tested. Findings Stepwise profiling was shown to be statistically significantly effective in reducing intradialytic episodes. Results demonstrated that linear sodium profiling was not effective in reducing hypotensive events during dialysis. Discussion This review has shown that using stepwise profiling is more effective at reducing intra-dialytic symptoms than other profiling methods. There was no evidence that linear profiling method was any more effective than conventional dialysis and in fact the results showed the reverse.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".