Clinical Measures Identify Vitamin D Deficiency in Dialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Vitamin D deficiency (defined by serum levels of 25-hydroxyvitamin D) is common in patients with ESRD on hemodialysis, but risk factors are unknown. This study was conducted to determine whether routinely measured clinical and demographic parameters could identify dialysis patients who are vitamin D deficient. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Nine-hundred eight patients with 25-hydroxyvitamin D levels were identified from the Accelerated Mortality on Renal Replacement (ArMORR) cohort of incident U.S. dialysis patients and were divided into training (60%) and validation (40%) sets. Predictive models were generated from routinely assessed clinical and demographic data in the training set using logistic regression modeling, neural networks, and decision trees with vitamin D deficiency as the dependent variable. Models underwent progressive variable reduction to identify the simplest model that remained predictive. RESULTS: Seventy-nine percent of the population was vitamin D deficient (25-hydroxyvitamin D <30 ng/ml). Black race, female sex, winter season, and hypoalbuminemia (serum albumin < or =3.1 g/dl) were the strongest predictors of vitamin D deficiency. In the validation set, the presence of hypoalbuminemia and winter season increased the likelihood of vitamin D deficiency in black women (from 90% to 100%), black men (from 85% to 100%), white women (from 82% to 94%), and white men (from 66% to 92%). CONCLUSIONS: Deficiency of 25-hydroxyvitamin D is nearly universal among patients with hypoalbuminemia initiating chronic hemodialysis in winter.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".