Acupuncture versus cognitive behavioral therapy for pain among cancer survivors with insomnia: an exploratory analysis of a randomized clinical trial
Bibliographic record
Abstract
Pain and insomnia often co-occur and impair the quality of life in cancer survivors. This study evaluated the effect of acupuncture versus cognitive behavioral therapy for insomnia (CBT-I) on pain severity among cancer survivors with comorbid pain and insomnia. Using data from the CHOICE trial that compared acupuncture versus CBT-I for insomnia among cancer survivors, we analyzed the effect of interventions on pain outcomes in 70 patients with moderate to severe baseline pain. Interventions were delivered over eight weeks. We assessed average pain severity (primary outcome) and pain interference at baseline, week 8, and week 20. We further defined insomnia and pain responders as patients who achieved clinically meaningful improvement in insomnia and pain outcomes, respectively, at week 8. We found that compared with baseline, the between-group difference (-1.0, 95% CI -1.8 to -0.2) was statistically significant favoring acupuncture for reduced pain severity at week 8 (-1.4, 95% CI -2.0 to -0.8) relative to CBT-I (-0.4, 95% CI-1.0 to 0.2). Responder analysis showed that 1) with acupuncture, insomnia responders reported significantly greater pain reduction from baseline to week 4, compared with insomnia non-responders (-1.5, 95% CI -2.7 to -0.3); 2) with CBT-I, pain responders reported significantly greater insomnia reduction at week 8, compared with pain non-responders (-4.7, 95% CI -8.7 to -1.0). These findings suggest that among cancer survivors with comorbid pain and insomnia, acupuncture led to rapid pain reductions, which contributed to a decrease in insomnia, whereas CBT-I had a delayed effect on pain, possibly achieved by insomnia improvement.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".