Vitamin D supplementation and mortality risk in chronic kidney disease: a meta-analysis of 20 observational studies
Bibliographic record
Abstract
BACKGROUND: Vitamin D insufficiency correlates with mortality risk among patients with chronic kidney disease (CKD). The survival benefits of active vitamin D treatment have been assessed in patients with CKD not requiring dialysis and in patients with end stage renal disease (ESRD) requiring dialysis. METHODS: MEDLINE, Embase, the Cochrance Library, and article reference lists were searched for relevant observational trials. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS) checklist. Pooled effects were calculated as hazard ratios (HR) using random-effects models. RESULTS: Twenty studies (11 prospective cohorts, 6 historical cohorts and 3 retrospective cohorts) were included in the meta-analysis., Participants receiving vitamin D had lower mortality compared to those with no treatment (adjusted case mixed baseline model: HR, 0.74; 95% confidence interval [95% CI], 0.67-0.82; P <0.001; time-dependent Cox model: HR, 0.71; 95% CI, 0.57-0.89; P <0.001). Participants that received calcitriol (HR, 0.63; 95% CI, 0.50-0.79; P <0.001) and paricalcitol (HR, 0.43 95% CI, 0.29-0.63; P <0.001) had a lower cardiovascular mortality. Patients receiving paricalcitol had a survival advantage over those that received calcitriol (HR, 0.95; 95% CI, 0.91-0.99; P <0.001). CONCLUSIONS: Vitamin D treatment was associated with decreased risk of all-cause and cardiovascular mortality in patients with CKD not requiring dialysis and patients with end stage renal disease (ESRD) requiring dialysis. There was a slight difference in survival depending on the type of vitamin D analogue. Well-designed randomized controlled trials are necessary to assess the survival benefits of vitamin D.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.044 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".