Quality of life in chronic pain is more associated with beliefs about pain, than with pain intensity
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to investigate pain cognitions and quality of life of chronic pain patients referred to a multi-disciplinary university pain management clinic and to search for predictors of quality of life. METHODS: A heterogeneous group of 1208 chronic pain patients referred to the Maastricht university hospital pain clinic participated in this cross-sectional study. At the initial assessment, all patients completed a set of questionnaires on demographic variables, cause, location, pain intensity (McGill pain questionnaire, MPQ), pain coping and beliefs (pain coping and cognition list, PCCL), pain catastrophising (pain catastrophising scale, PCS) and eight dimensions of quality of life (Rand-36). RESULTS: The results showed that the present sample of heterogeneous pain patients reported low quality of life on each domain and significantly lower scores than has been found in previous studies with other Dutch chronic pain populations. Patients with low back pain and multiple pain localisations experienced most functional limitations. Women reported more pain, more catastrophising thoughts about pain, more disability and lower vitality and general health. When tested in a multiple regression analysis, pain catastrophising turned out to be the single most important predictor of quality of life. Especially social functioning, vitality, mental health and general health are significantly associated with pain catastrophising. CONCLUSIONS: Patients from a multi-disciplinary university pain clinic experience strikingly low quality of life, whereby low back pain patients and patients with multiple pain localisations have the lowest quality of life. Pain catastrophising showed the strongest association with quality of life, and stronger than pain intensity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".