Effects of Vitamin D Intake on FEV1 and COPD Exacerbation: A Randomized Clinical Trial Study
Bibliographic record
Abstract
AIM: This study aimed to evaluate the effects of vitamin D intake on COPD exacerbation and FEV1 in the patients with severe and very severe COPD. METHODS: This double blind placebo control randomized clinical trial study was done in the Ashayer university hospital in Khorramabad in 2012. Eighty eight patients with severe and very severe COPD were randomly selected from those who recoursed to the internal medicine clinic of Ashayer hospital. They were randomly allocated to case and placebo group. The patients received routine treatment for COPD. Along with the routine treatment, placebo group received 100,000 IU of oral vitamin D per month, for 6 months. Data was analyzed using SPSS computer software, paired t-test, independent t-test, non parametric t-test and Pearson correlation coefficients. RESULTS: In each group, there were 44 patients. After the intervention, there were significant differences in FEV1 and the number of COPD exacerbation between the case and control group patients. Also, after the study, in the case group, FEV1 was increased and the number of COPD exacerbation was decreased significantly. CONCLUSION: Vitamin D intake decreased COPD exacerbation and improved FEV1 in the patients with severe and very severe COPD. It is suggested that baseline serum vitamin D levels will recorded in similar studies and the effect of vitamin D intake will evaluated regarding the baseline serum vitamin D levels.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".