Pain phenotypes in endometriosis: a population-based study using latent class analysis
Bibliographic record
Abstract
Objective To identify phenotypes of pain in patients with endometriosis and to investigate their associations with predictors and quality of life (QoL). Design Population-based study. Setting A referral university center in Quebec City, Canada. Population or Sample A total of 352 patients aged 18‒50 years and diagnosed with endometriosis. Methods Latent class analysis (LCA) was used to identify pain phenotypes. To assess the associations, the three-step approach of LCA was applied. Main Outcome Measures Pain phenotypes, predictors of pain phenotypes, QoL. Results A total of 352 patients were included in the analyses. The diagnosis of endometriosis was either based on histology (N=135), imaging (N=106) or clinical presentation (N=111). The optimal model identified two distinct and homogeneous phenotypes of patients with endometriosis. The two groups had distinct clinical presentations, one with more severe and frequent pain symptoms and poorer quality of life (54%); the other with mild and less frequent pain symptoms (46%). Predictors of a high pain phenotype were a previous treatment failure, use of pain killers, a family history of endometriosis, a low annual family income, and pain comorbidities such as painful bladder, fibromyalgia, migraines, low back pain, irritable bowel syndrome, anxiety, and depression or mood disorders. The presence of endometrioma was predictive of the low pain phenotype. Phenotype membership was associated with distinct quality of life profiles (p<0.001). Conclusion Patients with endometriosis and pelvic pain can be grouped into two distinct and homogeneous phenotypes. Phenotypes membership correlates with quality of life and can be predicted with the patients’ characteristics. These findings will need to be validated in other populations and may inform the development of more specialized or personalized interventions based on the pain phenotypes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".