Deep Dyspareunia: Review of Pathophysiology and Proposed Future Research Priorities
Bibliographic record
Abstract
INTRODUCTION: Dyspareunia has been traditionally divided into superficial (introital) dyspareunia and deep dyspareunia (pain with deep penetration). While deep dyspareunia can coexist with a variety of conditions, recent work in endometriosis has demonstrated that coexistence does not necessarily imply causation. Therefore, a reconsideration of the literature is required to clarify the pathophysiology of deep dyspareunia. AIMS: To review the pathophysiology of deep dyspareunia, and to propose future research priorities. METHODS: A narrative review after appraisal of published frameworks and literature search with the terms (dyspareunia AND endometriosis), (dyspareunia AND deep), (dyspareunia AND (pathophysiology OR etiology)). MAIN OUTCOME VARIABLE: Deep dyspareunia (present/absent or along a pain severity scale). RESULTS: The narrative review demonstrates potential etiologies for deep dyspareunia, including gynecologic-, urologic-, gastrointestinal-, nervous system-, psychological-, and musculoskeletal system-related disorders. These etiologies can be classified according to anatomic mechanism, such as contact with a tender pouch of Douglas, uterus-cervix, bladder, or pelvic floor, with deep penetration. Etiologies of deep dyspareunia can also be stratified into 4 categories, as previously proposed for endometriosis specifically, to personalize management: type I (primarily gynecologic), type II (nongynecologic comorbid conditions), type III (central sensitization and genito-pelvic pain/penetration disorder), and type IV (mixed). We also identified gaps in the literature, such as lack of a validated patient-reported questionnaire or an objective measurement tool for deep dyspareunia and clinical trials not powered for sexual outcomes. CONCLUSION: We propose the following research priorities for deep dyspareunia: deep dyspareunia measurement tools, inclusion of the population avoiding intercourse due to deep dyspareunia, nongynecologic conditions in the generation of deep dyspareunia, exploration of sociocultural factors, clinical trials with adequate power for deep dyspareunia outcomes, partner variables, female sexual response, pathways between psychological factors and deep dyspareunia, and personalized approaches to deep dyspareunia. Orr N, Wahl K, Joannou A, et al. Deep Dyspareunia: Review of Pathophysiology and Proposed Future Research Priorities. Sex Med Rev 2020;8:3-17.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".