Expectations predict chronic pain treatment outcomes
Bibliographic record
Abstract
Accumulating evidence suggests an association between patient pretreatment expectations and numerous health outcomes. However, it remains unclear if and how expectations relate to outcomes after treatments in multidisciplinary pain programs. The present study aims at investigating the predictive association between expectations and clinical outcomes in a large database of chronic pain patients. In this observational cohort study, participants were 2272 patients treated in one of 3 university-affiliated multidisciplinary pain treatment centers. All patients received personalized care, including medical, psychological, and/or physical interventions. Patient expectations regarding pain relief and improvements in quality of life and functioning were measured before the first visit to the pain centers and served as predictor variables. Changes in pain intensity, depressive symptoms, pain interference, and tendency to catastrophize, as well as satisfaction with pain treatment and global impressions of change at 6-month follow-up, were considered as treatment outcomes. Structural equation modeling analyses showed significant positive relationships between expectations and most clinical outcomes, and this association was largely mediated by patients' global impressions of change. Similar patterns of relationships between variables were also observed in various subgroups of patients based on sex, age, pain duration, and pain classification. Such results emphasize the relevance of patient expectations as a determinant of outcomes in multimodal pain treatment programs. Furthermore, the results suggest that superior clinical outcomes are observed in individuals who expect high positive outcomes as a result of treatment.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".