Vitamin A vs no supplementation: Chronic lung disease
Supplemental vitamin A showed no clear difference in chronic lung disease (oxygen use at 28 days in survivors) compared with no supplementation (moderate certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Supplemental vitamin A vs no supplementation Chronic lung disease (oxygen use at 28 days in survivors) · very low birth weight infants Supplemental vitamin A showed no clear difference in chronic lung disease (oxygen use at 28 days in survivors) compared with no supplementation (moderate certainty). Vitamin A supplementation to prevent mortality and short‐ and long‐term morbidity in very low birth weight… (2016) · Cochrane Headline outcomes only | RR 0.93 [0.86, 1.01] | 7 studies · 1070 participants | Moderate |
Supplemental vitamin A vs no supplementation Chronic lung disease (oxygen use at 36 weeks' postmenstrual age in survivors) · very low birth weight infants Supplemental vitamin A probably leads to lower chronic lung disease (oxygen use at 36 weeks' postmenstrual age in… compared with no supplementation (moderate certainty). Vitamin A supplementation to prevent mortality and short‐ and long‐term morbidity in very low birth weight… (2016) · Cochrane Headline outcomes only | RR 0.87 [0.77, 0.99] | 5 studies · 986 participants | Moderate |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Vitamin A supplementation to prevent mortality and short‐ and long‐term morbidity in very low birth weight infants 2016 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.