Mindful Self-Compassion to Reduce Pain Interference Among Adults with Osteogenesis Imperfecta
Bibliographic record
Abstract
Between 60 and 80% of adults with osteogenesis imperfecta (OI) experience chronic pain and associated interference. Currently available pain therapies often provide marginal efficacy. Mindful self-compassion (MSC) has emerged as a promising intervention for coping with chronic pain. We conducted a single-center 8-week pilot intervention study to assess the feasibility and acceptability of a MSC program among adults with OI and co-occurring chronic pain. Individuals attended the validated MSC course consisting of 8 weekly virtual 2-h sessions. Participants completed a battery of validated questionnaires assessing pain, various aspects of well-being, and physical function at baseline and post-intervention. Participants wore the ActiGraph GT9X Link watch to measure sleep duration and sleep efficiency. Seven adults with OI and co-occurring pain participated in the MSC program. The program was feasible, as indicated by high attendance and high questionnaire completion rates. Participants reported a mean ± standard deviation (SD) of 3.5 out of 5 ± 0.4 on the Intervention Acceptability Framework. 86% (6/7) of participants found the MSC program to be acceptable. While our pilot study was not powered to show efficacy, we observed a decrease in pain interference on the PROMIS pain interference questionnaire (mean 55.9 ± 5.5 at baseline vs. 50.0 ± 7.3 at 8 weeks). Implementation of the MSC program is feasible as a potential therapeutic option to address chronic pain in OI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".