Understanding the sleep-pain relationship in patients with interstitial cystitis/bladder pain syndrome
Bibliographic record
Abstract
INTRODUCTION: Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic pelvic pain condition with critical symptoms of urinary urgency and frequency, persistent bladder-related pain, and reduced quality of life. Poor-quality sleep can lead to significant disturbances in daily life and increased pain in IC/BPS patients. Resilience, depressive symptoms, and pain catastrophizing have univariate associations with sleep and pain in IC/BPS, suggesting they may be mechanisms in this sleep and pain relationship. METHODS: This online study recruited patients self-reporting a diagnosis of IC/BPS through support groups, social media posts (Facebook, Reddit, and Instagram), and urology clinic advertisements. Participants completed questionnaires on demographics, urologic symptoms, pain, pain catastrophizing, depressive symptoms, and resilience. Only those participants who met the RAND Interstitial Cystitis Epidemiology (RICE) criteria for IC/BPS diagnosis were included. A multiple mediation model was first examined, followed by a serial mediation model. RESULTS: = 47.0, standard deviation [SD ] 16.7, range 18-83 years) met inclusion criteria. A multiple mediation model showed greater sleep disturbance was associated with greater pain severity through depressive symptoms and pain catastrophizing, but not resilience (b=0.79, bootSE =0.26, bootCI [0.33, 1.35]). A serial mediation showed that the sleep-to-pain relationship had a significant indirect effect through pain catastrophizing and depressive symptoms (b=0.78, bootSE =0.26, bootCI [0.35, 1.32]). CONCLUSIONS: Findings suggest depressive symptoms and pain catastrophizing may be important psychosocial mechanisms in the sleep-to-pain relationship. These results help guide future sleep and pain research in IC/BPS and aid in developing and refining treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".