Association between vitamin D receptor activator and the risk of infection‐related hospitalizations among incident hemodialysis patients: a nested case–control study
Bibliographic record
Abstract
BACKGROUND: Patients suffering from chronic kidney disease are at greater risk of developing infection than the normal population, and infections are the second cause of mortality after cardiovascular complications in this population. Some reports suggest that the intake of active vitamin D might be beneficial to prevent infections. Therefore, we aimed to determine if the oral intake of vitamin D receptor activator (VDRA) is associated with a lower risk of infection-related hospitalization (IRH) among incident chronic hemodialysis patients. METHODS: We conducted a nested case-control study in a cohort of 4933 patients initiating chronic hemodialysis between 1 January 2001 and 31 December 2007 in Quebec, Canada, using administrative databases. We identified cases of hospital admission indicating an infection as main diagnosis on the hospital's discharge sheet. Up to 10 controls were randomly selected for each case. Association between oral VDRA use and risk of IRH was estimated using conditional logistic regression. RESULTS: We identified 1136 cases of IRH and 10396 controls during the study period. The intake of VDRA was not associated with the risk of being hospitalized due to an infection (odds ratio [OR], 1.07; 95% confidence interval [CI], 0.95-1.20). Using the prior 6-month cumulative dose of VDRA, we also found that a cumulative VDRA dose of less than 45 mcg (OR, 1.05; 95%CI, 0.92-1.19) or greater than 45 mcg (OR, 1.15; 95%CI, 0.96-1.36) was not associated with the IRH risk. CONCLUSIONS: The oral intake of VDRA was not associated with the risk of IRH in incident 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".