The Impact of Stress and Anxiety on Dysmenorrhea Severity: A systematic review
Bibliographic record
Abstract
Primary dysmenorrhea (PD) affects a significant proportion of menstruating individuals, with stress and anxiety increasingly recognized as modifiable factors influencing pain severity. This systematic review synthesizes evidence on the relationship between stress/anxiety and dysmenorrhea severity, exploring underlying mechanisms and clinical implications. Following PRISMA guidelines, we conducted a comprehensive search across PubMed, Web of Science, Scopus, and ScienceDirect. Included studies assessed stress (perceived stress, PTSD, occupational stress) or anxiety (generalized/trait anxiety) in individuals with PD, using validated scales. Risk of bias was evaluated using the Newcastle-Ottawa Scale and Cochrane RoB 2.0. Of 2,305 screened records, 45 studies met inclusion criteria, with 13 analyzed in detail. Cross-sectional studies (n=12) predominated, alongside one RCT study. Significant associations between stress/anxiety and PD severity (e.g., OR=2.8 for perceived stress (95% CI: 1.9–4.1); r=0.782, p<0.001 for stress-pain correlation). PTSD and occupational stress exacerbated dysmenorrhea (β=10.48, p=0.001; p <0.05, respectively). Neurobiological mechanisms (e.g., amygdala hypertrophy, HPA axis dysfunction) were implicated in pain amplification. Stress and anxiety are significantly associated with dysmenorrhea severity, potentially mediated by neuroendocrine and inflammatory mechanisms.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".