Effects of acupuncture versus cognitive behavioral therapy on brain-derived neurotrophic factor in cancer survivors with insomnia: an exploratory analysis
Bibliographic record
Abstract
INTRODUCTION: Decreased brain-derived neurotrophic factor (BDNF) is associated with poor sleep. This study examined the effects of acupuncture versus cognitive behavioral therapy for insomnia (CBT-I) on serum BDNF and sleep outcomes in cancer survivors with insomnia. METHODS: This was an exploratory analysis of a randomized clinical trial (n = 160) comparing acupuncture versus CBT-I for cancer survivors with insomnia. Interventions were delivered over 8 weeks. Outcomes were assessed at baseline and week 8. Serum BDNF was evaluated with enzyme-linked immunosorbent assay (ELISA). Sleep was evaluated with the insomnia severity index and consensus sleep diary. Pearson correlations between BDNF and sleep outcomes were calculated. Data analysis was limited to 87 survivors who provided serum samples. RESULTS: Among 87 survivors, the mean age was 61.9 (SD: 11.4) years, 51.7% were women, and 24.1% were non-White. Mean serum BDNF did not significantly increase in acupuncture (n = 50) or CBT-I (n = 37) groups. When analysis was restricted to patients with low baseline BDNF (i.e. levels below the sample median of 47.1 ng/mL), the acupuncture group (n = 22) demonstrated a significant 7.2 ng/mL increase in mean serum BDNF (P = 0.03), whereas the CBT-I group (n = 21) demonstrated a non-significant 2.9 ng/mL increase (P = 0.28). Serum BDNF was not significantly correlated with sleep outcomes (all P > 0.05). CONCLUSION: NCT02356575 (ClinicalTrials.gov).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".