Hypomagnesemia as a predictor of mortality in hemodialysis patients and the role of proton pump inhibitors: A cross‐sectional, 1‐year, retrospective cohort study
Bibliographic record
Abstract
Introduction This study aimed to evaluate the association between proton pump inhibitor (PPI) use and serum magnesium levels, and the role of hypomagnesemia and PPI use as a risk factor for mortality in hemodialysis patients. Methods An observational study, including a cross-sectional and 1-year retrospective cohort study. The study comprised 399 hemodialysis patients at a single center, and was conducted from January to September 2014. Multiple linear regression analysis was used to investigate the independent relationship between serum magnesium levels and baseline demographic and clinical variables, including PPI and histamine-2 receptor antagonist use. Cox regression model was used to identify lower serum magnesium level and PPI as a predictor of 1-year mortality. Findings Serum magnesium levels were lower with PPI use than non-PPI use (2.39 ± 0.36 vs. 2.56 ± 0.39 mg/dL, P < 0.001). Multiple linear regression analysis showed that PPI use, low serum albumin levels, and low serum potassium and high-sensitivity C-reactive protein (hs-CRP) levels were significantly associated with low serum magnesium levels. A total of 29 deaths occurred during the follow-up period. According to Cox regression analysis stratified by hs-CRP, only high serum hs-CRP levels (>4.04 mg/L) in association with low serum magnesium levels was an independent risk factor for 1-year mortality (hazard ratio: 2.92; 95% CI: 1.53-6.40, P < 0.001). Discussion Serum magnesium levels are lower in PPI use. In the inflammatory state, a low serum magnesium level is a significant predictor of mortality in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".