Abstract TMP86: Folic Acid Therapy Prevents Stroke in Countries Without Mandatory Folic Acid Food Fortification: A Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Additional folic acid treatment appears to have a neutral effect on reducing vascular risk in countries that mandate fortification of food with folic acid (e.g. US and Canada). However, it is uncertain whether folic acid therapy reduces stroke risk in countries without folic acid food fortification. Objective: To comprehensively evaluate the efficacy of folic acid therapy on stroke prevention in countries without folic acid food fortification. Methods: Pubmed, EMBASE, and Clinicaltrials.gov from January 1966 to August 2015 were searched to identify relevant studies. Relative risk (RR) with 95% CI was used as a measure of the association between folic acid supplementation and risk of stroke, after pooling data across trials in a random-effects model. Results: The search identified 14 randomized controlled trials involving treatment with folic acid that had enrolled 71,334 participants, all of which stroke was reported as an outcome measure. Across all trials, folic acid therapy vs. control was associated with a lower risk of stroke (RR=0.84; 95% CI, 0.76-0.93; P=0.0007) and there was no heterogeneity among include trials (P for heterogeneity =0.27, I2=17%) (Figure). The RR of stroke in primary prevention trials was 0.81 (12 trials, 95% CI, 0.72-0.91; P=0.0006). Among 3 trials, which distinguished ischemic from hemorrhagic stroke as endpoints, folic acid therapy reduced risk of ischemic stroke (RR=0.77; 95% CI 0.67-0.90, P=0.0006), but had a neutral effect on hemorrhagic stroke (RR=0.92, 95% CI 0.66-1.28, P=0.61). Conclusion: Among people living in countries without mandatory folic acid food fortification, folic acid therapy reduces the occurrence of a stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.059 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".