Genitopelvic Pain: A Scoping Review of Studies in Minoritized Samples
Bibliographic record
Abstract
Limited genitopelvic pain research has focused on racially/ethnically, sexually, and gender/sex minoritized samples despite its high prevalence among these groups. The aim of this scoping review was to examine genitopelvic pain research published on racially/ethnically, sexually, and gender/sex minoritized samples. After removal of duplicates, the abstracts of 1,330 articles were screened, and 974 were excluded. Of 356 remaining studies, 227 were included for data extraction. Results indicated that genitopelvic pain is common among minoritized groups, that they often fare worse in psychosocial and sexual wellbeing, and that they develop a variety of coping strategies. Also, some racially/ethnically minoritized groups report higher pain severity, describe their pain differently than detailed in clinical guidelines, and report medical mistrust in their healthcare interactions. In addition, sexually minoritized women who identify as bisexual or partner with men often reported higher frequency of pain. Most studies focused on cisgender women with genitopelvic pain; studies that focused on men were primarily concerned with anodyspareunia in cisgender men who have sex with men or with genitopelvic pain in trans men post gender-affirming surgery. Future work should inform the updating of existing clinical guidelines, frameworks, and validated measures in a culturally sensitive and inclusive manner.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".