Characterizing the description of pelvic congestion syndrome pain: A latent class analysis
Bibliographic record
Abstract
Objectives Chronic pelvic pain from pelvic congestion syndrome (PCS) is a complex condition disproportionately affecting women. PCS pain has been described as dull and achy, but emerging research indicates variances in the historical pain depictions. We aimed to identify the groups of pain characteristics experienced by women living with PCS using a latent class analysis and examine their predictive validity on quality of life, pain intensity, and pain management indicators. Methods A secondary data analysis of cross-sectional survey data collected from 160 participants on a Facebook PCS support group was conducted. After evaluating the original 86 unique pain descriptors endorsed on the McGill Pain Questionnaire, descriptors endorsed by more than 30 participants were retained for analysis ( n = 34). Results Results from the latent class analysis identified two latent classes: mild but consistent (44.4%) and intense and debilitating (55.6%). Between the two latent classes, there were clear patterns of pain endorsement to indicate that women in the two groups experience PCS pain differently. Compared to the second latent class (intense and debilitating), women in the first latent class (mild but consistent) experienced milder PCS associated pain and reported a significantly higher quality of life, satisfaction with their health, and less interference with sleep quality and sexual desire. Unfortunately, everyday activities (i.e., exercising, urinating, moving, standing, and working) were more likely to increase pain for women in the second latent class. Conclusions Diagnosis and treatment of pelvic venous disorders are hindered by outdated evidence on the expected pain depictions. A comprehensive pain profile of PCS is needed to establish the effect on women’s lifestyles, quality of life, and mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".