Folic Acid in Stroke Prevention in Countries without Mandatory Folic Acid Food Fortification: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Additional folic acid (FA) treatment appears to have a neutral effect on reducing vascular risk in countries that mandate FA fortification of food (e.g., USA and Canada). However, it is uncertain whether FA therapy reduces stroke risk in countries without FA food fortification. The purpose of this study was to comprehensively evaluate the efficacy of FA therapy on stroke prevention in countries without FA food fortification. METHODS: PubMed, EMBASE, and clinicaltrials.gov from January 1966 to August 2016 were searched to identify relevant studies. Relative risk (RR) with 95% confidence interval (CI) was used as a measure of the association between FA supplementation and risk of stroke, after pooling data across trials in a random-effects model. RESULTS: The search identified 13 randomized controlled trials (RCTs) involving treatment with FA that had enrolled 65,812 participants, all of which stroke was reported as an outcome measure. After all 13 RCTs were pooled, FA therapy versus control was associated with a lower risk of any future stroke (RR, 0.85; 95% CI, 0.77 to 0.95). FA alone or combination of FA and minimal cyanocobalamin (≤0.05 mg/day) was associated with a lower risk of future stroke (RR, 0.75; 95% CI, 0.66 to 0.86) whereas combination of FA and cyanocobalamin (≥0.4 mg/day) was not associated with a lower risk of future stroke (RR, 0.95; 95% CI, 0.86 to 1.05). CONCLUSIONS: FA supplement reduced stroke in countries without mandatory FA food fortification. The benefit was found mostly in patients receiving FA alone or combination of FA and minimal cyanocobalamin.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.051 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".