Serum Folic Acid and Erectile Dysfunction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction The association between folic acid (FA) and erectile dysfunction (ED) was contradictory in the published original articles, and no meta-analysis was conducted to pool these data. Aim To verify the role of FA in the pathology of ED and explore the treatment efficacy of FA for ED patients. Methods An extensive search was performed on PubMed, Cochrane Library, and Web of Science to obtain all relevant studies published up to October 31, 2020. Studies comparing the serum FA level between ED patients and healthy controls, or comparing the score of the IIEF-5, or IIEF before and after folic acid therapy alone or combination in ED patient were eligible for our meta-analysis. The Newcastle-Ottawa Scales (NOS) was used to qualify included studies. Main Outcome Measures The standardized mean differences (SMD) and their corresponding 95% confidence intervals (95% CIs) were calculated to pool our data. Results Nine studies were eligible for our meta-analysis to verify the association between FA and ED, and to explore the treatment efficacy of FA for ED patients. The pooled SMD of the FA level difference between ED patients and healthy subjects was -0.94 (95% CI: -1.59, -0.30, P = .004). Moreover, the level of folic acid in healthy subjects, Mild ED patients, Moderate ED patients and Severe ED patients was 11.847 (95%CI = 9.671, 14.022), 9.496 (95%CI = 8.425, 10.567), 6.597 (95%CI = 5.187, 8.007) and 5.623 (95%CI = 3.535, 7.711) respectively. The SMD of changes in score of IIEF-5 was 1.89 with 95%CI (1.60, 2.17) after FA administration in ED patients. Our analysis also showed that combination therapy of FA plus tadalafil changed the score of IIEF with 0.90 (95%CI = 0.44, 1.36) comparing to combination of placebo plus tadalafil. Conclusion This novel meta-analysis demonstrated that FA was an independent risk factor for ED and FA supplement may have potentially positive effects in the treatment of ED patients.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".