Can pain catastrophizing be changed in surgical patients? A scoping review
Bibliographic record
Abstract
Background: Catastrophizing, a coping style characterized by an exaggerated negative affect when experiencing or anticipating pain, is an important factor that adversely affects surgical outcomes. Various interventions have been attempted with the goal of reducing catastrophizing and, by extension, improving treatment outcomes. We performed a systematic review to determine whether catastrophizing can be altered in surgical patients and to present evidence for interventions aimed at reducing catastrophizing in this population. Methods: Using a scoping design, we performed a systematic search of MEDLINE and Embase. Studies reporting original research measuring catastrophizing, before and after an intervention, on the Pain Catastrophizing Scale (PCS) or Coping Strategies Questionnaire (CSQ) were selected. Studies were assessed for quality, the nature of the intervention and the magnitude of the effect observed. Results: We identified 47 studies that measured the change in catastrophizing score following a broad range of interventions in surgical patients, including surgery, patient education, physiotherapy, cognitive behavioural therapy, psychologist-directed therapy, nursing-directed therapy and pharmacological treatments. The mean change in catastrophizing score as assessed with the PCS ranged from 0 to –19, and that with the CSQ, from +0.07 to –13. Clinically important changes in catastrophizing were observed in 7 studies (15%). Conclusion: Catastrophizing was observed to be modifiable with an intervention in a variety of surgical patient populations. Some interventions produced greater reductions than others, which will help direct future research in the improvement of surgical outcomes.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".