Pain-related Activity Management Patterns and Function in Patients With Fibromyalgia Syndrome
Bibliographic record
Abstract
OBJECTIVES: To clarify the importance of avoidance, pacing, and overdoing pain-related activity management patterns as predictors of adjustment in patients with fibromyalgia syndrome. METHODS: A total of 119 tertiary care patients with fibromyalgia syndrome who agreed to be part of an activity management pain program completed a survey, which requested information about demographics, pain intensity and pain interference, psychological and physical function, and pain-related activity management patterns. Hierarchical regression analyses were used to identify the unique contributions of the 3 different pain-related activity management patterns (avoidance, pacing, and overdoing) to the prediction of pain interference, psychological function, and physical function. RESULTS: The avoidance pattern was a significant and unique predictor of worse psychological and physical function as well as greater pain interference. Pacing was significantly associated with less pain interference and better psychological function, whereas overdoing was not found to predict patient functioning. DISCUSSION: The findings confirm the importance of pain-related activity management patterns as predictors of patient function, and support the necessity of addressing these factors in chronic pain treatment. In addition, the results suggest that targeting increases in activity pacing and decreases in pain avoidance, specifically, might yield the best patient outcomes. However, further research to evaluate this possibility is necessary.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".