Evaluating the efficacy of graded in vivo exposure for the treatment of fear in patients with chronic back pain: A randomized controlled clinical trial
Bibliographic record
Abstract
Psychological treatments for chronic pain, particularly those based upon cognitive behavioural principles, have generally been shown to be efficacious. Recently, a treatment has been developed based upon the fear-avoidance model of chronic musculoskeletal pain, which suggests chronic pain can be relieved by exposing the individual to movements and tasks that have been avoided due to fear of (re)injury. This graded in vivo exposure treatment has been found to be beneficial in case studies. The present investigation utilized a randomized controlled trial method to assess the effectiveness of graded in vivo exposure relative to other conditions. Forty-four chronic low back pain patients were randomly assigned to graded in vivo exposure, graded activity, or a wait-list condition. While only trend differences were observed for pain-related disability, patients in the graded in vivo exposure condition demonstrated (a) significantly greater improvements on measures of fear of pain/movement, fear avoidance beliefs, pain-related anxiety, and pain self-efficacy when compared to those in the graded activity condition, and (b) significantly greater improvements on measures of fear-avoidance beliefs, fear of pain/movement, pain-related anxiety, pain catastrophising, pain experience, and anxiety and depression when compared to those in the wait-list control condition. Additionally, patients in the graded in vivo exposure condition maintained improvements in these areas at one month follow-up. Implications of these findings for the treatment of individuals with chronic low back and other pain conditions are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".