The Influence of Coping-oriented Hypnotic Suggestions on Chronic Pain in Patients with Spinal Cord Injury (SCI): A Randomized Controlled Study
Bibliographic record
Abstract
Background and aims: Coping-oriented hypnotic suggestions aimed at reducing pain catastrophizing have been shown to reduce pain in people with chronic tension-type headache and experimental pain in healthy volunteers during hypnosis (Kjøgx et al., 2016). However, the effect on pain post-hypnosis is unknown. The aim is to investigate the effect of coping-oriented hypnotic suggestions on chronic pain post-hypnosis. Methods: Seventy-five SCI-patients with chronic pain (>3, NRS 0-10) are randomized into one of three conditions; 1) coping-oriented hypnosis plus current treatment, 2) neutral hypnosis plus current treatment, or 3) current treatment only. The following variables are assessed before intervention and over a period of 14 days post-intervention: Pain intensity/unpleasantness (NRS 0-10), pain impact (on mood, daily activities and sleep; NRS 0-10), coping strategies related to pain (Coping Strategies Questionnaire), pain catastrophizing (Pain Catastrophizing Scale), anxiety and depression (Hospital Anxiety and Depression Scale). Patients’ global impression of change and side effects of the hypnosis are also assessed for 14 days post-intervention. Results: Preliminary results will be presented at the congress. Conclusions: If coping-oriented hypnosis is found to reduce pain for a substantial period post-hypnosis, this form of hypnosis may provide an alternative to medication or may be used in conjunction with lower medication dosages. References: Kjøgx, H., Kasch, H., Zachariae, R., Svensson, P., Jensen, T.S., Vase, L. (2016). Experimental manipulations of pain catastrophizing influence pain levels in chronic pain patients and healthy volunteers. Pain 157(6), 1287-1296.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".