The Temporal Relationship between Catastrophizing and Chronic Pain
Bibliographic record
Abstract
Introduction/Aim: Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) is a chronic pelvic pain syndrome characterized by persistent pain localized to the bladder and urologic symptoms of urgency, frequency, and dysuria (Nickel et al., 2009). While the temporal relationship between catastrophizing and chronic pain has been examined in other chronic pain populations (e.g., Campbell et al., 2012), the present study is the first to investigate this temporal relationship in an IC/BPS sample. Methods: 151 women diagnosed with IC/BPS were recruited from tertiary care urology clinics and completed the Short Form McGill Pain Questionnaire and the Pain Catastrophizing Scale at baseline (Time 1), 6 months post-baseline (Time 2), and 12-months post-baseline (Time 3). Two cross-lagged panel analysis were conducted using residualized change scores. Results: Increases in catastrophizing between Time 1 and Time 2 (Early Catastrophizing Change) predicted increases in pain between Time 2 and Time 3 (Later Pain Change), after controlling for early changes in pain and later changes in catastrophizing. In contrast, increases in pain between Time 1 and Time 2 (Early Pain Change) did not predict increases in catastrophizing between Time 2 and Time 3 (Later Catastrophizing Change), after controlling for early changes in catastrophizing and later changes in pain. Discussion/Conclusions: Early increases in catastrophizing predict later increases in pain levels among patients with IC/BPS, but not vice versa. These findings add to the growing body of research emphasizing the importance of catastrophizing in the development and maintenance of chronic pain. Clinical implications include targeting catastrophizing for the management of IC/BPS pain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".