Treatment Expectations—You Get What You Expect—and Depression Plays a Role
Bibliographic record
Abstract
Positive treatment expectations demonstrably shape treatment outcomes regarding pain and disability in patients with chronic low back pain. However, knowledge about positive and negative treatment expectations as putative predictors of interindividual variability in treatment outcomes is sparse, and the role of other psychological variables of interest, especially of depression as a known predictor of long-term disability, is lacking. We present results of the first prospective study considering expectations in concert with depression in a sample of 200 patients with chronic low back pain undergoing an inpatient interdisciplinary multimodal pain therapy. We analyzed the characteristics of pain and disability, treatment expectation, and depression assessed at the beginning (T0), at the end of (T1), and at 3-month follow-up (T2) of interdisciplinary multimodal pain therapy. Treatment expectations did emerge as a significant predictor of changes in pain intensity and disability, respectively, showing that positive expectations were associated with better treatment outcomes. Mediation analyses revealed a partially mediating effect of treatment expectations on the relation between depression and pain outcomes. PERSPECTIVE: These results expand knowledge regarding the role of treatment expectations in individual treatment outcome trajectories in chronic pain patients, paving the way for much-needed efforts toward optimizing patient expectations and personalized approaches in clinical settings.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".