Inter-rater reliability of Mechanical Diagnosis and Therapy (MDT) in evaluating and classifying chronic pelvic pain syndrome
Bibliographic record
Abstract
INTRODUCTION: Chronic pelvic pain syndrome (CPPS) involves complex interactions between the musculoskeletal system, nervous system, and psychosocial factors. A major challenge in managing CPPS is the lack of reliable assessment and classification systems. The Mechanical Diagnosis and Therapy (MDT) is a widely used and reliable classification system for assessing and managing painful musculoskeletal conditions affecting the spine and extremities. This study's primary objective was to assess the inter-rater reliability of the MDT assessment in diagnosing CPPS using clinical vignettes. Secondary objectives included determining the prevalence of MDT classification categories. METHODS: Five MDT clinicians classified clinical vignettes into three categories: 1) Spinal Derangement, 2) Pelvic Floor Contractile Dysfunction, or 3) MDT OTHER subgroups. The vignettes were developed from the McKenzie Pelvic Pain Assessment Form. Inter-rater reliability among clinicians was calculated using the Fleiss kappa statistic with 95% confidence intervals, and Cohen's kappa examined reliability between pairs of raters. RESULTS: < 0.001). Inter-rater reliability was higher when classifying female vignettes (Fleiss kappa = 0.658, 95% CI = 0.634, 0.682) than male vignettes (Fleiss kappa = 0.546, 95% CI = 0.519, 0.573). The most common classification was Spinal Derangement (57%), followed by MDT OTHER subgroups (26%) and Pelvic Floor Contractile Dysfunction (17%). CONCLUSIONS: The study indicates good inter-rater reliability among MDT clinicians in classifying pelvic pain syndrome. However, clinical vignettes may not fully capture the complexities of real participant interactions, potentially inflating agreement. Future studies should incorporate direct observation of real participant encounters alongside clinical vignettes to improve validity.
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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 | 0.061 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".