Understanding the physical and mental functioning of those with persistent and resolved Complex Regional Pain Syndrome to help inform treatment approaches
Bibliographic record
Abstract
Background \nPain is bio-psychosocial in nature. Recognising the inter-relationship of physical and psychological factors in Complex Regional Pain Syndrome (CRPS) has clinical implications for treating this often poorly understood chronic pain condition. Whilst there is little evidence suggesting psychosocial factors have a prognostic role in the development of CRPS, research suggests CRPS has significant consequences for patients, such as poor psychological health, poor functional ability and reduced quality of life (QoL). It is also known that anxiety, pain-related fear, and disability in the early stages of CRPS are associated with poorer outcomes in the first year. \nHowever, little is known about how demographic and bio-psychosocial factors may vary and relate to patient-defined recovery and other individual characteristics (including duration of the condition and upper- versus lower-limb CRPS). Understanding self-reported symptoms and psychosocial factors within these contexts may be important in helping healthcare professionals design and evaluate future treatments. \n \nAims \nTo undertake a cross-sectional study, describing the self-reported physical and psychological function of people with CRPS (with varying disease duration), and those who had recovered. Using an international sample to ensure population diversity, to identify variances and/or relationships between recovered and non-recovered populations and condition characteristics (upper/lower limb affected). \n \nMethods \nFollowing ethical and institutional approvals at study sites, potential participants ≥18yrs, who met, or previously met, Budapest CRPS criteria, were identified from 8 country-specific databases (UK, Germany, USA, Canada, Switzerland, Denmark, Netherlands, Poland) and sent postal questionnaires (translated from English as required). Consent was implied by return of completed questionnaires. Data collected within countries was anonymised and pooled on a single common database (lead centre Bath). \nSelf-reported data included: patient demographics, limb affected, disease stage (early, intermediate, late, recovered/non-recovered); knowing CRPS type (I or II); date of CRPS onset; trigger (trauma or spontaneous); and symptoms (from a provided list) in the prior 48 hours. \nParticipants also completed: McGill Pain Questionnaire (MPQ); EQ-5D; Acceptance and Action Questionnaire (AAQ-II); Radboud Skills Questionnaire (RSQ) for upper-limb CRPS; and Measuring Activity Limitations in Walking Questionnaire (WAQ) for lower-limb. Chi-squared, Welch’s t-tests, odds ratios and regression analyses were used to explore the data. \n \nResults \nN=347 participants (80.4% female; mean age=53yrs; 52.7% disease duration ≥ 3yrs. N=310 reported recovery status: 280 (90.3%) non-recovered. \nSelf-reported recovery and number of symptoms were strongly associated (χ2 = 124.94, df = 15, p<.001). There were no associations with demographics, disease duration, trigger, limb affected but positive associations with knowing CRPS type (p < .05) and having caring responsibilities (p < .05). \nNon-recovered participants had lower psychological flexibility/higher avoidance (AAQ-II), higher pain (MPQ) and lower QoL (EQ-5D) (p ≤ .001). \nNon-recovered participants with lower-limb CRPS more frequently reported hyperalgesia, allodynia, hair changes, involuntary muscle movements than those with upper-limb CRPS and had poorer MPQ (p < .01), EQ-5D (p < .05), SF-36 Physical Functioning (p < .001) and Energy/Fatigue (p < .05) scores. \nFunction (RSQ), AAQ-II, and number of symptoms were jointly (p < .001) and individually (p < .05) predictive of EQ-5D scores for non-recovered participants with upper-limb CRPS. \n \nConclusion \nUnresolved CRPS has negative consequences for mental and physical well-being, with people with lower-limb CRPS having the poorest health outcomes. Disease duration and demographic characteristics did not influence health status but a small “recovered” sample (n=30) should be noted. \nLower psychological flexibility/higher avoidance in the non-recovered cohort maybe of relevance for treatment interventions, and supports a multi-disciplinary rehabilitation approach which promotes increased physical function conducted with concurrent psychological support. Education about CRPS and responsibilities for others may support recovery from CRPS.
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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 | 0.011 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".