Ergocalciferol Versus Cholecalciferol in Non-Dialysis Dependent Chronic Kidney Disease Patients: A Small Retrospective Cohort Study
Bibliographic record
Abstract
PURPOSE: The purpose of this retrospective cohort study was to measure the difference between cholecalciferol and ergocalciferol in their ability to effect vitamin D, parathyroid hormone (PTH), calcium, and phosphorous serum concentrations in patients with stage 3 or 4 chronic kidney disease. METHODS: This was a retrospective cohort study conducted within a single-center ambulatory nephrology clinic. Patients eligible for the study were identified through medical records displaying each patient's initiation on either ergocalciferol or cholecalciferol from 2013 to 2016. Patients' baseline vitamin D, PTH, calcium, and phosphorous serum concentrations were taken prior to treatment initiation, and patients were reassessed with a second measurement within 12 months of therapy. RESULTS: Out of 149 eligible patients, 110 were excluded. There were 33 patients included on cholecalciferol and 6 patients on ergocalciferol. A significant difference was observed in the percent change of phosphorous serum concentrations from baseline following drug administration (p=0.03). The mean changes from baseline to final serum phosphorous concentrations (mg/dL) were 0.12 and -0.3 for cholecalciferol and ergocalciferol, respectively. There was no significant difference in vitamin D (14.9, 15.1, p=0.97), PTH (5.6, 2.3, p=0.72), or calcium (0.05, -0.17, p=0.08) serum concentrations between cholecalciferol and ergocalciferol, respectively. There was a statistically significant increase in the mean change in serum phosphorous concentrations within the cholecalciferol group compared to the ergocalciferol group. CONCLUSION: In this small pilot study, cholecalciferol treatment appeared to increase serum phosphorous concentrations compared to ergocalciferol. These observations may warrant further large-scale studies that are appropriately powered to validate such findings.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".