Biopsychosocial Characteristics of Patients With Chronic Pain Expecting Different Levels of Pain Relief in the Context of Multidisciplinary Treatments
Bibliographic record
Abstract
OBJECTIVES: Evidence suggests that patients' expectations predict chronic pain treatment outcomes. Although patients vary in terms of expected pain relief, little is known about individual factors related to such variations. This study aims to investigate how patients with various levels of pain relief expectations differ on the basis of biopsychosocial baseline characteristics in the context of multidisciplinary chronic pain treatment. MATERIALS AND METHODS: Data from 3110 individuals with chronic pain attending one of 3 multidisciplinary pain treatment centers were considered. Participants completed a self-reported measure of pain relief expectations and provided information pertaining to biological, psychological, and social variables. RESULTS: A backward stepwise regression helped identify biopsychosocial variables that significantly predicted expected pain relief. Subsequent analyses suggest that patients reporting low, moderate, high, and very high expectations of pain relief differed significantly in terms of pain duration and depressive symptoms. Significant between-group differences were also found with regard to overall physical health, age, sex, and ethnicity. DISCUSSION: Identifying characteristics related to different levels of pain relief expectations is a fundamental step in generating a more comprehensive understanding of how expectations can be of use in the successful management of chronic pain conditions.
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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 | 0.001 | 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) | 0.002 | 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".