Acupuncture versus cognitive behavioral therapy for anxiety among cancer survivors with insomnia: An exploratory analysis of a randomized clinical trial
Bibliographic record
Abstract
Background Anxiety and insomnia frequently co-occur among cancer survivors and are strongly interconnected, yet no widely accepted intervention simultaneously targets both symptoms. Methods Data were drawn from a dual-center, parallel-group, randomized, comparative effectiveness trial evaluating acupuncture versus CBT-I for insomnia. Seventy-six participants with baseline Hospital Anxiety and Depression Scale-Anxiety (HADS-A) scores of ≥8 were included. Both interventions were administered over eight weeks, with follow-up until 20 weeks. Anxiety was assessed at baseline, week 8, and week 20 using HADS-A. A linear mixed-effects model was used to examine mean change in HADS-A scores. Additionally, responder analyses were conducted, with insomnia and anxiety responders defined as patients demonstrating clinically meaningful improvements in either outcome by week 8. Results: Both CBT-I and acupuncture significantly reduced HADS-A scores at week 8 (CBT-I -3.75; acupuncture: -3.14) and week 20 (CBT-I: -3.05; acupuncture: -2.66) compared to baseline (all p < 0.001). There was no between-group difference (p=0.85). In responder analyses, CBT-I showed greater anxiety reduction in insomnia responders (-4.62) than non-responders (-0.45), at both time points (week 8: p =0.0046; week 20: p =0.038). In the acupuncture group, the difference in anxiety reduction between insomnia responders (-3.96) and non-responders (-1.58) was not statistically significant. Conclusion Both acupuncture and CBT-I effectively manage comorbid anxiety and insomnia in cancer survivors. Acupuncture may address these symptoms independently, while CBT-I may improve them in an interconnected manner. Trial registration ClinicalTrials.gov registration (NCT02356575)
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".