Effects of weight‐based ultrafiltration rate limits on intradialytic hypotension in hemodialysis
Bibliographic record
Abstract
INTRODUCTION: High ultrafiltration (UF) rates can result in intradialytic hypotension and are associated with increased mortality. The effects of a weight-based UF rate limit on intradialytic hypotension and the potential for unwanted fluid weight gain and hospitalizations for volume overload are unknown. METHODS: This retrospective cohort study examined 123 in-center hemodialysis patients at one facility who transitioned to 13 mL/kg/h maximum UF rates. Patients were studied for an 8 week UF rate limit exposure period and compared to the 8-week period immediately prior, during which the cohort served as its own historical control. The primary outcomes were frequency of intradialytic hypotension events and percentage of treatments with a hypotension event. FINDINGS: The delivered UF rate was lower during the exposure compared to the baseline period (mean UF rate 7.90 ± 4.45 mL/kg/h vs. 8.92 ± 5.64 mL/kg/h; P = 0.0005). The risk of intradialytic hypotension was decreased during the exposure compared to baseline period (event rate per treatment 0.0569 vs. 0.0719, OR 0.78 [95% CI 0.62-1.00]; P = 0.0474), as was the risk of having a treatment with a hypotension event (percentage of treatments with event 5.2% vs. 6.8%, OR 0.75 [95% CI 0.58-0.96]; P = 0.0217). Subgroup analyses demonstrated that these findings were attributable to patients with high baseline UF rates. Statistically significant differences in all-cause or volume overload-related hospitalization were not observed during the exposure period. DISCUSSION: A weight-based UF rate limit of 13 mL/kg/h was associated with a decrease in the rate of intradialytic hypotension events among in-center hemodialysis patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".