Pilot Study of Intensive Pain Rehabilitation, Sleep, and Small-World Brain Networks in Adolescents with Chronic Pain
Bibliographic record
Abstract
Background: Approximately 25% of adolescents live with chronic pain, with many reporting symptoms of functional impairment and poor sleep quality. Both chronic pain and poor sleep quality can negatively impact brain functional connectivity and efficiency. Better sleep quality may improve pain outcomes through its relationship with brain functional connectivity. Methods: This pilot prospective cohort study used data from 24 adolescents with chronic pain (aged 10–18 years) participating in an Intensive Interdisciplinary Pain Treatment (IIPT) at the Alberta Children’s Hospital. Data were collected within the first couple of weeks prior to starting IIPT and on the last day of the 3-week IIPT program. Sleep quality was assessed using the modified Adolescent Sleep-Wake Scale. Resting-state functional MRI data were obtained, and graph-theory metrics were applied to assess small-world brain networks. Questionnaires were used to obtain self-reported functional disability data. Paired t-tests were applied to evaluate changes in outcomes from pre- to post-IIPT, and moderation analyses were used to examine the relationships between sleep, small-world brain network connectivity, and functional disability. Results: Total sleep quality (p = 0.005) increased, and functional disability (p = 0.020) decreased, between baseline and discharge from IIPT. Small-world brain networks did not change pre- to post-IIPT (p > 0.05). Unlike adolescents with high small-worldness (p = 0.665), adolescents with low to moderate small-world brain characteristics (1SD below or at the mean) who reported better sleep quality reported less functional disability (all p ≤ 0.001) over time. Conclusions: The IIPT program was associated with improvements in sleep quality and functional disability. Better sleep quality together with greater small-worldness was associated with less pain-related disability. This suggests that it is equally important for IIPTs to target sleep problems in adolescents with chronic pain, as this may have a key role in producing long-term improvements in pain outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".