P-310 Comorbidity profiles in women with endometriosis in the ComPaRe-Endometriosis cohort
Bibliographic record
Abstract
Abstract Study question To identify comorbidity profiles in women with endometriosis in ComPaRe-Endometriosis cohort Summary answer This study identified distinct comorbidity profiles (7) in women with endometriosis What is known already Endometriosis is associated with an increased risk of various chronic diseases, including autoimmune, atopic, psychiatric, and pain-related conditions. Although many studies have examined the links between endometriosis and individual diseases, very few have focused on associations with multiple comorbidities Study design, size, duration Study design: Cross-sectional study Size: 10,761 women with endometriosis Duration: E-cohort initiated in 2018. Analysis conducted on data at inclusion Participants/materials, setting, methods ComPaRe-Endometriosis is a prospective e-cohort initiated in 2018 and following over 10,000 women with endometriosis. Participants self-reported their chronic conditions at baseline from a list of 87 diseases. We conducted a cross-sectional study and used hierarchical clustering to identify baseline comorbidity profiles, and we described the socioeconomic, lifestyle, disease, and patient characteristics associated with each profile. Main results and the role of chance Among 10,761 participants, 64.2% reported only endometriosis, while 35.8% reported at least one comorbidity (1 comorbidity: 52.7%, 2: 22.9%, 3: 10.2%, 4: 5.4%, 5+: 8.8%). We identified seven clusters of participants: Cluster 1 (n = 8,656) included endometriosis patients with less comorbidities; Cluster 2 (n = 693) included mostly those with chronic pain (44.3%), irritable bowel syndrome (40.4%), and/or fibromyalgia (33.3%); Cluster 3 (n = 193) those with depression (99.5%) and/or anxiety (14.0%); Cluster 4 (n = 532) those with asthma (57.9%), allergies (36.5%), and/or chronic skin diseases (30.6%); Cluster 5 (n = 185) those with ovarian cysts/polycystic ovary syndrome (100%); Cluster 6 (n = 299) those with thyroid diseases (100%); and Cluster 7 (n = 203) those with migraine (100%) Limitations, reasons for caution Limitation: -Self reported diagnoses - Representativeness of the sample (the cohort of patients cannot be generalized to the general population) Wider implications of the findings These findings highlight the need for multidisciplinary care addressing the complex interactions between endometriosis and multiple physiological systems, offering new perspectives for improving disease management and guiding future research Trial registration number No
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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".