Dose of paricalcitol inversely associates with risk of hospitalization
Bibliographic record
Abstract
Background: Among patients receiving chronic HD, therapy with paricalcitol (Z) was associated with reduced mortality and morbidity compared to treatment with calcitriol (C). Furthermore, patients who did not receive any vitamin D therapy experienced highest mortality and morbidity. This study examined the hypothesis of a relationship between the dose of Z and subsequent hospitalization, more specifically, that patients receiving lower doses of Z would have a higher risk of being hospitalized. Methods: We performed a retrospective cohort study of patients new to HD who received treatment with Z or C between Jan 1999 and Dec 2001 using Poisson regression models. The primary exposure variable was the average dose/day, examined as categorical data by quintiles, during a 3‐month and then 12‐month follow‐up period excluding the days in hospitals. This latter exclusion was made due to uncertainty of the treatment during the period of hospitalization. Additional covariates included vitamin‐D group (Z vs. C), age, gender, race, and diabetes status, serum albumin, alkaline phosphatase, calcium, phosphorus, and iPTH. Results: We first examined dose of Z and C over a 3‐month period and then hospitalizations over the ensuing year. We did not find a dose‐response relationship in these analyses – i.e., dose over the first three months of dialysis is not associated with increased or reduced risk of hospitalizations during the ensuing 12 months. We then examined average dose of Z or C over the entire year and risk for hospitalization during the same year. Compared to total doses, average doses (total dose over the entire year divided by number of dialysis sessions during the same year) are less prone to bias. Risk of hospitalizations according to dose (lowest dose, Quintile 1) of injectable vitamin D is shown in the table below: Quintile HR 95% CI 1 1.093 1.025–1.165 2 1.073 1.007–1.143 3 1.055 0.990–1.124 4 1.050 0.978–1.116 5 1.0 REF Compared to those who received the highest doses of injectable vitamin D, those receiving the lowest had a 9% increased risk for a hospitalization. At each level, the risk for hospitalizations was 4% lower with Z compared to C. Conclusion: This pilot study indicates that higher average doses of Z are associated with a lower risk of hospitalization and suggests additional beneficial effects of vitamin D beyond mineral metabolism and PTH control.
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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 | 0.001 |
| 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 | 0.001 |
| 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".