P02.147. Emotions matter: sustained reductions in chronic non-structural pain after a brief, manualized emotional processing program
Bibliographic record
Abstract
An initial individual session was conducted by one of the authors (HS) to assess medical conditions and review life stressors and symptom progression in order to identify linkages between stressors and symptoms. The treatment program consisted of 4 weekly small group 2-hour sessions. Components included readings, writing about emotions, mindfulness and emotional awareness exercises (on CD), and other techniques to help people identify and process emotions related to stress and pain. Homework (e.g., writing, mindfulness exercises) was assigned daily. Patients were assessed at baseline by a research team and at post-treatment and 6-month follow-up. Included instruments were the Brief Pain Inventory (BPI) and the McGill Pain Questionnaire (MPQ). Fifty-nine adults with chronic musculoskeletal pain, primarily headaches, neck, back, and widespread pain (fibromyalgia), were included. Individuals with significant structural disease processes were excluded. Baseline demographics and characteristics were 76% women (mean age 51 years). 91% Caucasian, mean duration of pain 8.8 years, and baseline pain level 5.03 (0-10). Percent improvement was calculated for pain (% change from baseline BPI score). At post-treatment 64% had ≥ 30% improvement and 43% had ≥ 50% improvement; at 6-months 67% had ≥ 30% improvement and 53% had ≥ 50% improvement. Mean BPI scores were 5.38, 3.04, and 3.03 (p< 0.001, d=-1.27), respectively. The MPQ showed similar reductions. This high rate of improvement may surpass that of cognitive-behavioral interventions for chronic pain. An approach focusing on confronting emotional contributions to pain appears beneficial. Studies with control groups are underway to determine if targeting unresolved stress and emotions offers an advance in the treatment of chronic non-structural pain.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.061 | 0.003 |
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".