Evidence for the use of complementary and alternative medicines during fertility treatment: a scoping review
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicines (CAM) are sometimes used by individuals who desire to improve the outcomes of their fertility treatment and/or mental health during fertility treatment. However, there is little comprehensive information available that analyzes various CAM methods across treatment outcomes and includes information that is published in languages other than English. METHOD: This scoping review examines the evidence for 12 different CAM methods used to improve female and male fertility outcomes as well as their association with improving mental health outcomes during fertility treatment. Using predefined key words, online medical databases were searched for articles (n = 270). After exclusion criteria were applied, 148 articles were analyzed in terms of their level of evidence and the potential for methodological and author bias. RESULTS: Surveying the literature on a range of techniques, this scoping review finds a lack of high quality evidence that complementary and alternative medicine (CAM) improves fertility or mental health outcomes for men or women. Acupuncture has the highest level of evidence for its use in improving male and female fertility outcomes although this evidence is inconclusive. CONCLUSION: Overall, the quality of the evidence across CAM methods was poor not only because of the use of research designs that do not yield conclusive results, but also because results were contradictory. There is a need for more research using strong methods such as randomized controlled trials to determine the effectiveness of CAM in relation to fertility treatment, and to help physicians and patients make evidence-based decisions about CAM use during fertility treatment.
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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 | 0.023 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".