Association of Body Mass Index and Dysmenorrhea: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background: Dysmenorrhea or menstrual pain is a commonly occurring disorder in reproductive age women with different proposed risk factors, including body mass index. Objective: This study aimed to investigate the association between body mass index and dysmenorrhea using a systematic review and meta-analysis approach. Methods: Academic databases Scopus, PubMed CENTRAL, Embase, ProQuest, Science Direct, and ISI Web of Science, and Google Scholar- were searched systematically from inception until the end of February 2020. Original researches published in English with observational designs were included to examine the association of body mass index and dysmenorrhea as the primary outcome. Newcastle Ottawa scale was used to evaluate the methodological quality of the studies. Due to the variation of reported data across studies, all data were converted to Pearson correlation coefficient and corrected by transforming to fisher’s Z score. Then meta-analysis was performed using a random-effects model with Der-Simonian and Laird method. Results: A total of 61 studies with 57,079 participants, of which 25,044 reported having dysmenorrhea, were included. While publication bias was probable, results were corrected using the fill & trim method. The updated results based on this method showed that pooled Fisher’s z-score for the association of body mass index and dysmenorrhea was 0.04 (95% CI: -0.009; 0.085). The pooled estimated effect size of correlation showed a trivial to slight correlation between body mass index and dysmenorrhea with corrected fisher’s z score of 0.12 (95% CI: 0.08; 0.17, I2=95%). Conclusion: No association was found between body mass index and dysmenorrhea. But this finding should be interpreted with caution considering the included studies' limitations.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".