Developing the <scp>FIGO</scp> ‐ <scp>IPPS</scp> “R U <scp>MOVVING SOMe</scp> ” classification system for female chronic pelvic pain
Bibliographic record
Abstract
The goal was to develop a pragmatic classification system for conditions associated with chronic pelvic pain (CPP), aiming to enhance diagnosis, management, education, and research of CPP. An international, multidisciplinary panel participated in a modified RAND/UCLA Delphi consensus. This panel included healthcare professionals, medical society representatives, experts, individuals with lived experience of pain, advocacy groups, researchers, educators, and journal editors. The Delphi process comprised three rounds: two online surveys and one virtual meeting. Participants scored their agreement with statements using a 9-point Likert scale (1 = strongly disagree, 9 = strongly agree). A priori criteria for consensus were defined as follows: agreement, a mean score ≥7 with <15% scoring ≤3; disagreement, a mean score ≤3 with <15% scoring ≥7. Responses not meeting these criteria were considered indeterminate and advanced for further refinement in subsequent rounds. In round 1, 65 of 77 (84.4%) stakeholders participated; round 2 consisted of responses from 54 (70.1%) stakeholders, and 34 (44.2%) stakeholders engaged in round 3. The Delphi process yielded broad consensus on the definition of CPP and a corresponding classification system with the acronym R U MOVVING SOMe. This novel system comprises 12 categories: Reproductive, Urinary, Musculoskeletal, Other (not otherwise classified), Vulvovaginal, Vascular, Idiopathic (no pain contributor identified), Neurologic, Gastrointestinal, Sensitization/Nociplastic, Overlapping pain conditions, and Mental health. The R U MOVVING SOMe classification system represents a significant step towards a standardized framework for evaluating CPP. The high level of engagement and consensus among a diverse group of international stakeholders underscores its future potential to improve communication, clinical practice, education, and research in this challenging field.
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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 | 0.076 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".