PD9-12 WHAT GENERATES PAIN CATASTROPHIZING IN IC/BPS?
Bibliographic record
Abstract
You have accessJournal of UrologyInfections/Inflammation of the Genitourinary Tract: Interstitial Cystitis1 Apr 2014PD9-12 WHAT GENERATES PAIN CATASTROPHIZING IN IC/BPS? Dean A. Tripp, J. Curtis Nickel, Laura Katz, Lesley K. Carr, and Robert Mayer Dean A. TrippDean A. Tripp More articles by this author , J. Curtis NickelJ. Curtis Nickel More articles by this author , Laura KatzLaura Katz More articles by this author , Lesley K. CarrLesley K. Carr More articles by this author , and Robert MayerRobert Mayer More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.794AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES IC/BPS is a chronic pelvic pain syndrome associated with pain, urological symptoms and disability, largely unresponsive to medical treatment. Catastrophizing is a well-established predictor of pain and pain-related outcomes in IC/BPS, but the mechanisms and predictors of catastrophizing in IC/BPS remain unknown. Illness perceptions have been identified as important precursors to appraisals and coping mechanisms, and remain unexamined for chronic pelvic pain. The aim of this study was to evaluate illness perceptions as a predictor of patient catastrophizing within a biopsychosocial model. METHODS Female patients with IC/BPS (n=70) were recruited from tertiary care urology clinics and completed questionnaires (demographics, O’Leary Sant, McGill Pain Questionnaire, Brief-Illness Perceptions Questionnaire, Pain Catastrophizing Scale). Bivariate zero-order correlations were run between variables to assess for multicollinearity. Hierarchical multivariable regression analyses were run in the prediction of pain catastrophizing and its subscales. RESULTS Patients ranged in age from 20-85 years (mean=51.51±17.25), were predominantly Caucasian (94.3%), having at least some university / college education (78.6%), and a mean length of diagnosis of 8.44±9.92 years. In step 1 of the regression, demographics (age, education and length of diagnosis) did not predict catastrophizing. In step 2, pain (β=0.32, p=0.01) and IC problems (β=0.25, p=0.05) were both significant predictors of catastrophizing (F=9.92, p<.001). In step 3, illness perceptions (β=0.49, p<0.001) was the lone predictor of catastrophizing (F=13.10, p<.001), over and above demographics, pain and IC symptoms/problems. In further sub-analyses, emotional illness perceptions (e.g., concern about illness, and emotional impact of illness) mediated the relationship between pain/symptoms and helplessness catastrophizing. CONCLUSIONS Illness perceptions (e.g., emotional) were significant in predicting helplessness catastrophizing in a biopsychosocial model. Understanding the precursors and mechanisms of catastrophizing is important, as recent research has shown that catastrophizing is amenable to change with psychological intervention in chronic pelvic pain (Tripp et al., 2011). Further understanding this relationship can help to improve patient interventions in order to decrease disability and improve IC/BPS quality of life. © 2014FiguresReferencesRelatedDetails Volume 191 Issue 4S April 2014 Page: e216 Advertisement Copyright & Permissions© 2014Metrics Author Information Dean A. Tripp More articles by this author J. Curtis Nickel More articles by this author Laura Katz More articles by this author Lesley K. Carr More articles by this author Robert Mayer More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".