Women with chronic pelvic pain can be stratified using multimodal assessment
Bibliographic record
Abstract
ABSTRACT: Chronic pelvic pain (CPP) is a common and burdensome symptom in women yet current clinical management frequently leaves many with persistent pain. The Translational Research in Pelvic Pain (TRiPP) project adopts a pain-focused strategy, aiming to better phenotype CPP through multimodal assessment. Here, we integrated questionnaire, physiological, and biological data to (1) determine whether perturbations in the function of pain-relevant systems in women with CPP can be demonstrated and (2) explore whether these data can stratify women with CPP into meaningful subgroups, independent of diagnostic group. Participants included 108 women, aged 18 to 50 years, with CPP including endometriosis-associated pain (EAP), bladder pain syndrome (BPS), comorbid EAP and BPS, and pelvic pain with no underlying pathology alongside 50 pain-free controls. Analyses were conducted in 3 stages: (1) group comparisons, (2) latent profile analysis to identify CPP subgroups, and (3) clinical characterization of resulting clusters. Compared with controls, CPP participants reported significantly greater fatigue, poorer sleep, higher anxiety, depression, pain catastrophising, and more childhood trauma (P < 0.01). However, no significant differences were observed in physiological measures. Latent profile analysis revealed 3 distinct CPP subgroups, differentiated by questionnaires rather than physiological measures. Cluster 1 represents a group with predominantly higher-impact pain compared with others, that may be associated with nociplastic mechanisms. These findings support alternative approaches to stratification of CPP separate from standard diagnostic groupings. The role of questionnaire measures in this stratification facilitates translation to clinical settings; however, further work is required to determine whether differing therapeutic approaches are appropriate for each cluster.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".