Clinical and psychological moderators of the effect of mindfulness-based cognitive therapy on persistent pain in women treated for primary breast cancer – explorative analyses from a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Mindfulness-based intervention has been found efficacious in reducing persistent pain in women treated for breast cancer. Little, however, is known about possible moderators of the effect. We explored clinical and psychological moderators of the effect on pain intensity previously found in a randomized controlled trial of mindfulness-based cognitive therapy (MBCT) with women treated for breast cancer with persistent pain. MATERIAL AND METHODS: A total of 129 women treated for breast cancer reporting persistent pain were randomized to MBCT or a wait-list control. The primary outcome of pain intensity (11-point numeric rating scale) was measured at baseline, post-intervention, three, and six months follow-up. Proposed clinical moderators included age, axillary lymph node dissection (ALND), radiotherapy, and endocrine treatment. Psychological moderators included psychological distress [the Hospital Anxiety and Depression Scale (HADS)], the adult attachment dimensions anxiety and avoidance [the Experiences in Close Relationships Short Form (the ECR-SF)], and alexithymia [the Toronto Alexithymia Scale (TAS-20)]. Multi-level models were used to test moderation effects over time, i.e. time × group × moderator. RESULTS: Only attachment avoidance (p = 0.03, d = 0.36) emerged as a statistically significant moderator. Higher levels of attachment avoidance predicted a larger effect of MBCT in reducing pain intensity compared with lower levels attachment avoidance. None of the remaining psychological or clinical moderators reached statistical significance. However, based on the effect size, radiotherapy (p = 0.075, d = 0.49) was indicated as a possible clinical moderator of the effect, with radiotherapy being associated with a smaller effect of MBCT on pain intensity over time compared with no radiotherapy. CONCLUSION: Attachment avoidance, and potentially radiotherapy, may be clinically relevant factors for identifying the patients who may benefit most from MBCT as a pain intervention. Due to the exploratory nature of the analyses, the results should be considered preliminary.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".