Hemodialysis prescription education decreases intradialytic hypotension
Bibliographic record
Abstract
BACKGROUND: Intradialytic hypotension (IDH) is associated with increased morbidity and mortality. We studied the impact of an education program and hemodialysis (HD) prescription optimization on the frequency of IDH. METHODS: We compared chronic HD patients during 2 retrospective time periods: a control period and the study period which occurred after 2 months of physician education and HD prescription optimization. Primary study outcomes were the frequency of HD sessions complicated by IDH, and the prevalence of IDH-prone patients. RESULTS: There were 91 and 82 patients in the control and study periods, respectively. In the study period, 11% (115/1107) of HD sessions were complicated by IDH vs. 17% (189/1103) in the control period (p = 0.0002). There was a decreased odds ratio for IDH in the study period compared with control (odds ratio [OR] = 0.59; 95% confidence interval [95% CI], 0.40-0.86; p = 0.007). Compared with control, more patients in the study period were prescribed at least 2 preventative strategies (42% vs. 61%, p = 0.02), including increased use of cool dialysate (55% vs. 89%, p<0.001). Cool dialysate reduced the odds of IDH by 50% (OR = 0.50; 95% CI, 0.30-0.86; p = 0.012). CONCLUSION: HD prescription education with concurrent use of multiple preventative strategies is associated with a significant decrease in IDH.
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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 | 0.000 | 0.005 |
| 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 | 0.000 | 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".