Impact of adenomyosis on the outcome of in vitro fertilization
Bibliographic record
Abstract
We recently read an article by Wang et al. in Acta Obstetricia et Gynecologica Scandinavica.1 The authors reviewed the available literature to quantitatively assess the impact of adenomyosis diagnosed by ultrasound on the outcome of in vitro fertilization.1 However, this study may have some methodological flaws. In the Material and methods section, the authors emphasize a comprehensive search of only three databases, namely Embase, PubMed, and the Cochrane Library. Other commonly used databases, such as PsycINFO, Google Scholar, Web of Science, CINAHL, and Scopus were not included. Chinese databases with large volumes of literature, such as China Knowledge Network and Wanfang, were also excluded without any plausible explanation. This might have led to the omission of relevant target literature. Broadening the search could improve the validity of the results and conclusions of this systematic review. In addition, we suggest that the authors provide a detailed search procedure and present it in tabular form in the text. In the article, the authors used the Newcastle–Ottawa scale2 to assess the quality of the studies. However, Stang has questioned the validity of this scoring tool because it is prone to highly arbitrary results.3 Therefore, we suggest that the authors use a more valid and stable tool for quality assessment, such as the Down and Black tool.4 Finally, most of the pooled outcomes in this meta-analysis are highly heterogeneous, which significantly reduces the credibility of the conclusions. Although the authors tried very hard to explain the sources of heterogeneity through various subgroup analyses, this does not fully address the issue. Therefore, we suggest that the authors consider an inverse variance heterogeneity model as an alternative to the random effects model. This model could reduce the known problems of random effects models—of underestimating statistical errors and giving incorrect overconfidence estimates—and so reflect the true magnitude of the effect of the results.5 Despite these shortcomings, this study may have a positive impact on current clinical practice guidelines. Future high-quality randomized controlled trials with larger sample sizes are needed to further confirm the conclusions of this systematic review and meta-analysis.
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 | 0.019 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".