A systematic review of flaxseed effectiveness on infertility in animal model. Systematic Review
Bibliographic record
Abstract
According to the extensive studies carried out in the direction of the effectiveness of flax seed in solving various causes of infertility, the present study was conducted with the aim of investigating the effect of flax seed on infertility by a systematic review method. The search for studies in national and international databases was conducted in English and Persian without time restrictions. To evaluate the quality of the articles, the Cochrane Collaboration Risk of Bias tool and the Review Manager Program software (Revman 5.3) were used. The findings indicate that among the 1245 articles in the initial search, 56 articles (sample size: 4988) were included in the study. The articles were completed from 1998 to 2022 in Iran, America, Canada, Egypt and other countries of the world. Among the reviewed articles about the effect of flaxseed on the causes of infertility, 25 articles are about male and female reproductive system and sex hormones, 12 articles are about PCOS, 12 articles are about egg enrichment, two articles are about fertility power, two articles on uterine myoma and 1 article on cancer were studied. In most studies, the positive effect of flaxseed on infertility was confirmed. The available evidence shows the effect of flaxseed on different causes of infertility, but it is recommended to conduct more studies on different causes of male and female infertility on human samples with a strong methodology and appropriate sample size to ensure the effect of this plant on humans .
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".