Pain Phenotypes in Endometriosis: A Population‐Based Study Using Latent Class Analysis
Bibliographic record
Abstract
OBJECTIVE: To identify pain phenotypes in patients with endometriosis and investigate their associations with demographics, clinical characteristics, comorbidities and pain-related quality of life (QoL). DESIGN: Cross-sectional, single-centre, population-based study. SETTING: Referral university centre in Quebec City, Canada. POPULATION: Patients diagnosed with endometriosis were enrolled consecutively between January 2020 and April 2024. METHODS: Latent class analysis was used to identify pain phenotypes. A three-step approach of latent class analysis, involving logistic regression models, was applied to assess the associations between pain phenotypes and demographics, clinical characteristics, comorbidities and pain-related QoL. MAIN OUTCOME MEASURES: Pain phenotypes; demographic, clinical and comorbidity predictors of phenotype membership; association between QoL and pain phenotypes. RESULTS: A total of 352 patients were included. Two pain phenotypes were identified with distinct clinical presentations: one (54% of the participants) with more severe and frequent pain symptoms and poorer QoL and the other (46% of the participants) with mild and less frequent pain symptoms. The high pain phenotype was associated with previous treatment failure, painkiller use, familial history of endometriosis, low annual family income and comorbidities, including painful bladder, fibromyalgia, migraines, lower back pain, irritable bowel syndrome, anxiety and depression or mood disorders. The presence of endometrioma was associated with the low pain phenotype. Phenotype membership was associated with distinct QoL profiles (p < 0.001). The mean QoL score was higher in the high pain phenotype (59; 95% CI, 56-62) than in the low pain phenotype (33; 95% CI, 29-37). CONCLUSION: Patients with endometriosis can be categorised into two distinct phenotypes that correlate with QoL and patient characteristics. Validation in other populations is necessary and could aid the development of specialised or personalised interventions.
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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 | 0.000 |
| 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".