Effect of Vitamin D supplementation to reduce respiratory infections in children and adolescents in Vietnam: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: It is uncertain whether vitamin D can reduce respiratory infection. OBJECTIVE: To determine whether vitamin D supplementation reduces influenza and other upper viral respiratory tract infections. METHODS: A total of 1300 healthy children and adolescents between the ages of 3 and 17 years were randomized to vitamin D (14 000 U weekly) or placebo for 8 months in Vietnam. The primary outcome was reverse transcriptase (RT)-PCR-confirmed influenza infection, and the coprimary outcome was multiplex PCR-confirmed non-influenza respiratory viruses. Participants, caregivers, and those assessing outcomes were blinded to group assignment. RESULTS: A total of 650 children and adolescents were randomly assigned to vitamin D and 650 to placebo. The mean baseline serum 25-hydroxyvitamin D levels were 65.7 nmol/L and 65.2 nmol/L in the intervention and placebo groups, respectively, with an increase to 91.8 nmol/L in the vitamin D group and no increase, 64.5 nmol/L, in the placebo group. All 1300 participants randomized contributed to the analysis. We observed RT-PCR-confirmed influenza A or B occurred in 50 children (7.7%) in the vitamin D group and in 43 (6.6%) in the placebo group (hazard ratio [HR]: 1.18, 95% CI: 0.79-1.78). RT-PCR-confirmed non-influenza respiratory virus infection occurred in 146 (22.5%) in the vitamin D group and in 185 (28.5%) in the placebo group (hazard ratio [HR]: 0.76, 95% CI: 0.61-0.94). When considering all respiratory viruses, including influenza, the effect of vitamin D in reducing infection was significant, HR: 0.81, 95% CI: 0.66-0.99. CONCLUSION: Vitamin D supplementation did not reduce the incidence of influenza but moderately reduced non-influenza respiratory viral infection.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".